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Forecast of pain degree of lumbar disc herniation based on back propagation neural network

  • Xinying Ren , Huanwen Liu , Shiji Hui , Xi Wang and Honglai Zhang EMAIL logo
Published/Copyright: September 8, 2023
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Open Life Sciences
From the journal Open Life Sciences Volume 18 Issue 1

Abstract

To further explore the pathogenic mechanism of lumbar disc herniation (LDH) pain, this study screens important imaging features that are significantly correlated with the pain score of LDH. The features with significant correlation imaging were included into a back propagation (BP) neural network model for training, including Pfirrmann classification, Michigan State University (MSU) regional localization (MSU protrusion size classification and MSU protrusion location classification), sagittal diameter index, sagittal diameter/transverse diameter index, transverse diameter index, and AN angle (angle between nerve root and protrusion). The BP neural network training model results showed that the specificity was 95 ± 2%, sensitivity was 91 ± 2%, and accuracy was 91 ± 2% of the model. The results show that the degree of intraspinal occupation of the intervertebral disc herniation and the degree of intervertebral disc degeneration are related to LDH pain. The innovation of this study is that the BP neural network model constructed in this study shows good performance in the accuracy experiment and receiver operating characteristic experiment, which completes the prediction task of lumbar Magnetic Resonance Imaging features for the pain degree of LDH for the first time, and provides a basis for subsequent clinical diagnosis.

Keywords: lumbar disc herniation; magnetic resonance imaging; prediction model; imaging feature; pain score

1 Introduction

Lumbar disc herniation (LDH) is a disease that occurs when the lumbar discs undergo degenerative changes, causing the annulus to break. As a result, the nucleus pulposus protrudes or extrudes through the posterior longitudinal ligament, either below or by passing through it, into the spinal canal. This compression of surrounding tissue can lead to some major symptoms, including low back pain (LBP), radiative pain in the legs, and numbness. The most commonly affected areas are the L4-L5 and L5-S1 discs, which account for approximately 90% of all LDH cases. The disease mainly affects people between the ages of 20 and 40, with a higher incidence in male, with a male-to-female ratio of about (10–15):1.

LBP is the predominant clinical symptom of LDH, frequently causing movement restriction in individuals aged below 45. This is the second leading cause of seeing a doctor, the fifth leading cause of hospitalization, and the third leading cause of operation [1]. The prevalence of chronic LBP among individuals aged 24–39 is 4.2%, while among individuals aged 20–59, it is 19.6% [2]. In 2015, the global prevalence of LBP among adults reached 7.3% [3]. LBP has become a major cause of disability worldwide [4,5], with approximately 1% of Americans experiencing long-term disability due to LBP, and an additional 1% experiencing temporary disability [1].

Intervertebral disc degeneration (IDD) increases with age, with over 80% of individuals aged 50 and above exhibiting degenerative changes in their intervertebral discs. Lumbar disc degeneration is widely recognized as a contributing factor to LBP. However, the pathophysiological mechanisms of discogenic LBP are not yet fully understood. Chemical and mechanical compression of nerve root inflammation is thought to be the source of neuropathic pain [6].

In the 1970s, the emergence of Magnetic Resonance Imaging (MRI) and Computed Tomography in the field of medical imaging provided advantageous techniques for diagnosing spinal disorders. Even today, MRI remains the most recommended and suitable imaging modality for diagnosing nerve root-related LDH [7]. Numerous researchers have utilized imaging techniques to reconstruct two-dimensional or three-dimensional images of the lumbar spine in LDH patients, conducting studies related to investigating the mechanisms of LDH development, evaluating treatment effectiveness, and guiding therapy through the analysis of imaging features. Their research methods encompass clinical statistical analysis as well as deep learning and machine learning techniques [8,9,10]. However, there is significant variation in the research findings, with some studies indicating a significant positive correlation between the severity of LDH and lower limb pain scores [11], while others have found that certain imaging features are unrelated to disc degeneration or pain [12].

Previous studies on the relationship between imaging features and symptoms of degenerative spinal diseases have predominantly used traditional statistical methods. Sasaki et al. employed multivariate logistic regression analysis to evaluate the association between paraspinal muscle fat infiltration and the incidence of LBP [13]. Dunsmuir et al. employed Pearson correlation analysis to investigate the direct correlation between patients’ pain scores, disability scores, and the size of disc herniation [14]. Ranger et al. utilized t-tests and Chi-square tests to assess the morphological characteristics of paraspinal muscles in individuals with unilateral LDH, by comparing the differences in paraspinal muscle morphology between the back pain group (VAS score >4) and the control group (VAS score ≤4) [15].

In recent years, owing to the ongoing advancements in artificial intelligence, researchers have also begun to explore the integration of machine learning with degenerative spinal diseases. Compared to traditional statistical methods, machine learning offers the advantage of higher model accuracy, especially when dealing with variables that exhibit complex interactions [16]. Eriksson et al. predicted pain scores in patients with LBP using a logistic regression model based on image features. The sensitivity/specificity values were 0.90/0.36, 0.88/0.4, and 0.93/0.33, with Area Under the Curve (AUC) values of 0.69, 0.75, and 0.73, respectively [17]. Su et al. developed a multi-task classification network based on ResNet50 for the automatic assessment and grading of LDH, central canal stenosis, and compressed lumbar nerve roots in lumbar axial MRI. The overall accuracies for the three tasks were 84.17% (74.16%), 86.99% (79.65%), and 81.21% (74.16%), respectively [18]. Abdollah et al. extracted gray-level co-occurrence matrix features from MRI images in the intervertebral disc and upper and lower endplate regions of 14 patients with chronic LBP and 14 healthy volunteers. They established a classification model using the random forest algorithm to distinguish between the pain group and the pain-free group [19]. Hopkins et al. analyzed cervical spine magnetic resonance images of 14 patients with myelopathic cervical spondylosis and 14 control subjects using machine learning, by training an artificial neural network model to predict the modified Japanese Orthopaedic Association scores for myelopathic cervical spondylosis [20]. Currently, artificial intelligence has been widely applied to symptom prediction in degenerative spinal diseases. However, we found that many of these studies relied on imaging omics features as inputs for the models. Nonetheless, imaging omics features cannot fully explain underlying biological mechanisms, and the specific clinical and pathological changes they correspond to are not yet known. This limitation will significantly impede subsequent research on the pathological mechanisms.

The aim of this study is to further explore the pathogenesis of pain in patients with LDH. It analyzes the imaging features of the surrounding tissues of the lumbar intervertebral disc and their correlation with the Oswestry disability index (ODI) scores in LDH patients. Significant correlated structural features will be selected to train a back propagation (BP) neural network model. The model will utilize the MRI imaging features of the lumbar spine to predict the degree of pain in LDH, and identify important imaging biomarkers for the assessment of LDH symptoms and guidance of treatment.

This study is organized as follows: Section 2 provides detailed information on the case sources, inclusion and exclusion criteria, collection of imaging features, collection of questionnaires, statistical analysis methods, and construction methods of the BP neural network. Section 3 presents the statistical analysis results of the basic characteristics of the cases, the statistical analysis results of imaging characteristics, the correlation analysis results between imaging characteristics and symptom scores, as well as the results of the BP neural network model. Section 4 primarily focuses on the analysis and discussion of imaging features that are strongly correlated with symptom scores, as well as the analysis and discussion of the experimental results of the neural network.

2 Materials and method

2.1 Research method

The aim of this study is to describe and analyze the demographic and imaging features of LDH. The goal is to statistically analyze the correlation between imaging features of the surrounding tissues of the lumbar disc and the severity of pain. Significant correlated structural features will be selected and used to train a BP neural network model to predict the degree of pain in LDH using lumbar spine MRI imaging features. The aim of this study is to identify important imaging biomarkers for assessing LDH symptoms and guiding treatment. The research method chosen is a case series analysis, which involves the description, analysis, and summary of a series of demographic, clinical, and epidemiological features of a group of individuals with the same disease. This analysis method is used to analyze the clinical manifestations of a specific disease, evaluate the effectiveness of prevention and treatment measures, and contribute to the accumulation of clinical information, summarization of clinical experience, and improvement of disease diagnosis and technological research.

2.2 Study subjects

2.2.1 Source of study subjects

Ninety cases of LDH patients who underwent lumbar spine MRI examinations at Guangdong Provincial Hospital of Traditional Chinese Medicine from February 2022 to April 2022 were collected as study subjects. The hospital had a total outpatient volume of 7.22 million visits in 2021, ranking second in China in terms of outpatient volume. LDH accounted for 25–40% of orthopedic inpatients with lower back and leg pain. The imaging department was equipped with GE Signa 3.0T and Siemens Verio 3.0T superconductive MRI machines, a dedicated medical imaging and image processing laboratory, a picture archiving and communication system (PACS) for medical image storage and communication, and high-performance image workstations capable of offline processing and comprehensive analysis software. All patients underwent lumbar spine MRI scans using Siemens Verio 3.0T superconductive MRI. The lumbar spine MRI images were stored in the form of digital imaging and communications in medicine files.

  1. Informed consent: Informed consent has been obtained from all individuals included in this study.

  2. Ethical approval: The research related to human use has been complied with all the relevant national regulations, institutional policies and in accordance with the tenets of the Helsinki Declaration, and has been approved by the Ethics Committee of Guangdong Provincial Hospital of Traditional Chinese Medicine (approval number: YE2022-037-01).

2.2.2 Inclusion criteria

  1. Age of 18–80 years old;

  2. All patients were diagnosed as LDH by medical history, physical examination, and imaging examination;

  3. Patients who had lumbar MRI in the past (within 1 month) were checked by PACS system of Guangdong Provincial Hospital of Traditional Chinese Medicine;

  4. Informed consent was obtained from the patients.

2.2.3 Exclusion criteria

  1. Those who had a history of lumbar surgery, which affected the judgment of the results;

  2. Patients with lumbar compression fracture;

  3. LDH caused by acute violent injury;

  4. Combined with other spinal diseases (including lumbar anterior spondylolisthesis, tumor, tuberculosis, infection, scoliosis or kyphosis, metabolic bone disease, etc.).

2.3 Collection of basic information

By accessing the medical record system of Guangdong Provincial Hospital of Traditional Chinese Medicine, basic information of LDH patients was collected, including disease name, hospitalization number/outpatient number, gender, age, height, weight, duration of illness, chief complaint, present medical history, past medical history, and Western medicine diagnostic basis.

2.4 Acquisition of imaging features

All patients underwent lumbar MRI scans with a Magnetom Trio 3.0T MRI scanner (Siemens, Germany). All patients were in the supine position. Scan parameters were pulse repetition time of 575 ms, echo time of 18.5 ms, field of view of 160 mm × 160 mm, voxel size of 0.4 mm × 0.4 mm × 4.0 mm, and layer spacing of 0.3 mm.

The imaging data were exported from the hospital’s PACS system, and the patients’ lumbar MRI images (including sagittal T1, T2 sequences, and transverse T2 scans) were collected. The imaging features data were collected by observing the image pictures to highlight the most severe segments and the ITK-SNAP software [21] was used to measure the highlighted segments in the image pictures.

2.4.1 Pfirrmann classification

Pfirrmann classification is the grade describing the degeneration of lumbar intervertebral disc, and the higher the grade, the more serious the degeneration. In this study, the modified Pfirrmann classification was selected, with a total of 8 grades [22,23] (Figure 1).

Figure 1 Pfirrmann classification.
Figure 1

Pfirrmann classification.

The position of the red frame is the diseased intervertebral disc. The degree of lumbar disc degeneration is described using the modified Pfirrmann grading system, which consists of 8 levels.

Level 1: The signal of nucleus pulposus and medial annulus fibrosus was uniform high signal, which was significantly different from that of cerebrospinal fluid and posterior annulus fibrosus, and the height of intervertebral disc was normal.

Level 2: The signal of nucleus pulposus and medial annulus fibrosus was high (stronger than presacral fat, less than cerebrospinal fluid) or there was horizontal fissure in nucleus pulposus. The signal difference between the inner and outer fibers of the posterior ring was obvious, and the height of the intervertebral disc was normal.

Level 3: The signals of the nucleus pulposus and the medial annulus fibrosus were high (lower than that of the anterior sacral fat), and the signals of the fibers inside and outside the posterior annulus were different, and the intervertebral disc height was normal.

Level 4: The signal of nucleus pulposus and medial annulus fibrosus was moderately high (slightly stronger than that of lateral annulus fibrosus), the difference of posterior annulus fibrosus signal was not obvious, and the height of intervertebral disc was normal.

Level 5: The signal of nucleus pulposus and medial annulus fibrosus was low and high signal (equal to lateral annulus fibrosus), and the signal difference between the medial and lateral fibers of the posterior annulus fibrosus was not obvious, and the height of intervertebral disc was normal.

Level 6: The signal of nucleus pulposus and medial annulus fibrosus was low signal, and the signal difference between the medial and lateral fibers of the posterior annulus fibrosus was not obvious, and the height of the intervertebral disc was reduced by <30%;

Level 7: The signal of nucleus pulposus and medial annulus fibrosus was low signal, and the signal difference between the medial and lateral fibers of the posterior annulus fibrosus was not obvious. The height of the intervertebral disc was reduced by 30%–60%.

Level 8: The signal of nucleus pulposus and medial annulus fibrosus was low signal, and the signal difference between the medial and lateral fibers of the posterior annulus fibrosus was not obvious, and the height of the intervertebral disc was reduced by >60%.

2.4.2 Modic classification

Modic classification [24,25,26] (Figure 2) describes the degree of vertebral endplate degeneration.

Figure 2 Modic classification.
Figure 2

Modic classification.

The position of the red frame is the diseased endplate. Type I changes (T1 low signal, T2 high signal) correspond to vertebral marrow edema. Type II changes (T1 high signal, T2 high signal or iso-signal) represent fatty replacement of the marrow. Type III changes (T1 low signal, T2 low signal) are observed in vertebral osteosclerosis.

2.4.3 Michigan State University (MSU) regional localization

The upper column is a schematic of the measurement method, and the lower column is the corresponding T2W TSE image. The anatomical level with the most prominent nucleus pulposus was selected from the T2 sequence. It is the evaluation criteria for the size and location of lumbar disc protrusions inside and outside the spinal canal. The specific location and degree of protrusion of the herniated nucleus pulposus within the spinal canal are defined by different region grades, with a total of ten region grades. The anatomical plane with the most prominent herniated nucleus pulposus is selected from the T2 sequence. Two lines are drawn on both sides of the superior and inferior articular processes, and if the protrusion does not exceed the line connecting the superior articular processes, it is classified as grade 1. If it does not exceed the line connecting the inferior articular processes but exceeds the line connecting the superior articular processes, it is classified as grade 2. If it exceeds the line connecting the inferior articular processes, it is classified as grade 3. According to the location of the protrusion, it is divided into four regions: central region (A region), adjacent region (AB region), outer region (B region), and far outer region (C region) [27] (Figure 3).

Figure 3 MSU regional localization.
Figure 3

MSU regional localization.

2.4.4 Sagittal diameter index (SI)

SI [28] (Figure 4) is calculated as the ratio of the maximum sagittal diameter (AB) of the disc protrusion to the maximum sagittal diameter (CD) of the spinal canal. It provides a measurement of the position of the protrusion apex within the spinal canal. A higher SI ratio indicates a more severe disc protrusion that occupies a larger portion of the spinal canal.

2.4.5 Sagittal diameter/transverse diameter index (STI) [28] (Figure 5)

Figure 4 Sagittal diameter index (SI).
Figure 4

Sagittal diameter index (SI).

Figure 5 Sagittal diameter/transverse diameter index.
Figure 5

Sagittal diameter/transverse diameter index.

STI [28] (Figure 5) is calculated as the ratio of the product of the maximum sagittal diameter (AB) and transverse diameter (EF) of the disc protrusion to the product of the maximum sagittal diameter (CD) and transverse diameter (GH) of the spinal canal. It provides a measurement of the extent to which the disc protrusion occupies the spinal canal. A higher STI ratio indicates a more severe disc protrusion with greater occupation of the spinal canal.

2.4.6 Transverse diameter index (TI)

TI is calculated as the ratio between the length of a parallel line (a) at the apex of the disc protrusion and the length of the posterior line (b) of the corresponding vertebra (Figure 6). This index provides a measure of the extent to which the disc protrusion occupies the spinal canal. A smaller TI ratio indicates a more severe disc protrusion with greater occupation of the spinal canal.

Figure 6 Transverse diameter index.
Figure 6

Transverse diameter index.

2.4.7 Angle between nerve root and protrusion

The AN angle is determined by the connection between the lateral edge of the herniated nucleus pulposus and the lateral edge of the vertebra, along with the extension line of the inner wall of the spinal canal [29,30] (Figure 7). A smaller angle correlates with a greater degree of nerve root compression, indicating more severe compression.

Figure 7 Angle between nerve root and protrusion.
Figure 7

Angle between nerve root and protrusion.

2.4.8 Thickness of ligamentum flavum

The ligamentum flavum is a ligament that connects the superior lamina to the superior and lateral edges of the inferior lamina, originating from the inferior and medial edges of the superior lamina [31] (Figure 8). In cases where the measurements of bilateral ligamentum flavum differ, the larger value is chosen as the measurement result.

Figure 8 Thickness of ligamentum flavum.
Figure 8

Thickness of ligamentum flavum.

2.5 ODI score collection

For eligible patients who met the research criteria, the research plan, objectives, significance, etc., were explained in detail, and their consent was obtained. After obtaining their willingness to participate, an online questionnaire (using the Wenjuanxing platform, https://www.wjx.cn) was used for questionnaire collection. The link to the online questionnaire, which combined the informed consent form and the ODI scoring form, was sent to the patients. Patients were required to read the informed consent form, provide an electronic signature, and click “Agree” to proceed to the formal answering interface. For older patients who were not proficient in operating the system, family members could assist them in completing the questionnaire.

The ODI scoring form [32] is used to assess the severity of pain in patients. It consists of 10 items and is rated on a 6-point scale, with scores ranging from 0 to 5. The scores for each item are summed to obtain a total score, calculated as (score obtained/5 × number of questions answered) × 100%. Scores below 15 indicate mild pain, scores between 15 and 30 indicate moderate pain, and scores above 30 indicate severe pain.

2.6 Statistical analysis

This study used SPSS Statistics 25.0 for data statistical analysis. The normality of quantitative variables was assessed using the Shapiro–Wilk test. If the data adheres to a normal distribution, it is presented as x ̅ ± s standard deviation. Pearson correlation analysis was employed for examining correlations. In cases where the data satisfy the assumption of homogeneity of variance, one-way analysis of variance was conducted on multiple independent samples, followed by paired comparisons using the Bonferroni method. Conversely, if the data do not conform to a normal distribution, it is presented as the median (quartile range) [M(P25, P75)], and subsequent correlation analysis is carried out using the Spearman rank correlation analysis. When the assumption of homogeneity of variance is violated, the Kruskal–Wallis H test is employed to compare multiple independent samples, followed by pairwise comparisons using Bonferroni correction. The adjusted significance level for all tests was set at P < 0.05. For qualitative variables deemed statistically significant, the count (percentage; %) was used for representation. Group comparisons were performed using the chi-square test or Fisher’s exact test, with Bonferroni correction applied for pairwise comparisons. The significance level of pairwise comparisons was adjusted using the α distribution method. Correlation analysis was utilized to examine the relationship between statistically significant image features identified from inter-group comparisons. Subsequently, various artificial neural network models were trained and employed for prediction using these noteworthy features.

2.7 Pain degree prediction model of LDH based on BP neural network model

The BP neural network model learns certain patterns from existing data and is used to predict subsequent classification tasks. Specifically, we perform ADASYN oversampling on the existing data to obtain augmented data. Then, in each round, we repeatedly train our neural network model through k-fold cross-validation (Figure 9).

Figure 9 Neural network training.
Figure 9

Neural network training.

2.7.1 Data pre-processing

After statistical analysis, Pfirrmann classification, MSU protrusion size grading, MSU protrusion location classification, SI, STI, TI, and AN angle were selected as the data parameters, and these data were subjected to certain processing. All data were individually standardized using the z-score normalization formula.

(1) X * = x – μ δ .

2.7.2 Data oversampling

Due to the limited sample size of only 90 cases, there is a need to employ oversampling techniques to increase the sample size and balance the frequency of different categories. Oversampling techniques typically involve two approaches. The first approach is random sampling, which involves randomly sampling within the data space. The second approach involves data synthesis, where new data are generated based on the relationships within the existing data. In this model, the ADASYN oversampling algorithm is used. ADASYN is an adaptive synthetic oversampling method and represents one approach to data synthesis.

2.7.3 k-fold cross validation

To tackle the challenge of limited data size, the model adopts the k-fold cross-validation method to train the artificial neural network (Figure 10). The value of k is set to 10. In each iteration, we divide all samples into 9/10 (81 cases) for training the neural network, while the remaining 1/10 (9 cases) is used as the test set.

Figure 10 Schematic representation of k-fold cross validation.
Figure 10

Schematic representation of k-fold cross validation.

2.7.4 BP neural network construction

The single hidden layer BP neural network is used (Figure 11). Each layer represents a collection of nodes, where the input layer represents the features and the output layer represents the target variable. In multi-classification tasks, the output layer has the same number of nodes as the predicted categories, and the value assigned to each node represents the corresponding class probability. The adjacent layers are connected by a parameterized edge, which transmits information. The parameters are denoted as the first dimension, which denotes the number of neurons in the previous layer, while the second dimension indicates the number of neurons in the next layer.

Figure 11 Schematic representation of a single hidden layer artificial neural network.
Figure 11

Schematic representation of a single hidden layer artificial neural network.

The input layer consists of seven neurons: Pfirrmann classification, MSU regional positioning (MSU protrusion size classification and MSU protrusion location classification), SI, STI, TI, AN angle, the hidden layer contains 16 neurons, and the output layer consists of 3 neurons (mild group, moderate group, and severe group) for classification output.

The activation function chosen is the Rectified Linear Unit (ReLU) activation function. The ReLU sets the output of some neurons to zero, resulting in network sparsity. It also reduces the interdependence of parameters, alleviating the occurrence of overfitting problems.

(2) ReLu ( x ) = max ( 0 , x ) .

In the output layer, the outputs are passed through the softmax function to obtain the confidence scores for each class, and the class with the highest confidence score is considered as the classification result.

(3) Softmax ( z i ) = e z i ∑ c = 1 C e z c .

The initial neural network is not stable, and its prediction efficiency is low. To improve the prediction accuracy of the neural network, we use the crossentropy loss function.

(4) Loss = − 1 N ∑ i = 0 N – 1 ∑ k = 0 K − 1 y i , k ln p i , k .

3 Results

This study included a total of 90 patients with LDH, including 46 males (51.1%) and 44 females (48.9%). The distribution of the herniated discs was as follows: L2/3: 1 case (1%), L3/4: 2 cases (2%), L4/5: 31 cases (34.4%), and L5/S1: 56 cases (62.2%). Based on the ODI, the patients were divided into the following groups: mild group with 40 cases (44.4%); moderate group with 29 cases (32.2%); and severe group with 21 cases (23.3%). A comprehensive statistical analysis was conducted on the gender, age, height, weight, and duration of illness among the three groups of enrolled cases (Table 1), and no statistically significant differences were observed (P > 0.05), allowing for intergroup comparisons.

Table 1

Gender, age, height, weight, and course of disease were compared among the groups

Gender Age (years) Height (cm) Weight (kg) Course of disease (month)
Male Female
Mild group 16 (40.00) 24 (60.00) 39.65 ± 11.84 164.94 ± 8.68 61.00 (50.50, 70.00) 6.00 (3.25, 35.50)
Moderate group 17 (58.60) 12 (41.40) 37.34 ± 11.28 166.83 ± 8.16 65.00 (57.00, 71.00) 7.00 (3.00, 36.00)
Severe group 13 (61.90) 8 (38.10) 40.05 ± 11.84 166.81 ± 10.61 70.00 (55.00, 74.50) 7.00 (3.00, 42.00)
X²/F/H 3.610 0.439 0.486 2.074 0.638
P 0.164 0.646 0.617 0.355 0.727

Gender distribution among the groups was analyzed using frequency counts, represented as (number [%]) and tested with a chi-square test (X 2 = 3.610, P = 0.164), showing no statistically significant differences. Shapiro–Wilk tests were conducted to assess normal distribution and tests for homogeneity of variances among the groups.

Among the mild group, moderate group, and severe group, there were significant difference in the overall mean value of Modic classification, Pfirrmann classification, MSU protrusion size classification, MSU protrusion location classification, SI, STI, TI, and AN angle. But there was no significant difference in ligamentum flavum thickness among the three groups (F = 1.499, P = 0.229 > 0.05), which was excluded as shown in Table 2.

Table 2

Overall statistical analysis of imaging features

Mild group Moderate group Severe group F/H/X² P
Modic classification None 34 (85.0) 18 (62.1) 8 (38.1) 18.631 0.001
Ⅰ 2 (5.0) 1 (3.4) 1 (4.8)
Ⅱ 3 (7.5) 10 (34.5) 11 (52.4)
Ⅲ 1 (2.5) 0 (0) 1 (4.8)
Pfirrmann classification 1 2 (5.0) 0 (0) 0 (0) 56.868 0.000
2 2 (5.0) 0 (0) 0 (0)
3 7 (17.5) 2 (6.9) 0 (0)
4 15 (37.5) 7 (24.1) 0 (0)
5 11 (27.5) 8 (27.6) 6 (28.6)
6 3 (7.5) 12 (41.4) 2 (9.5)
7 0 (0) 0 (0) 9 (42.9)
8 0 (0) 0 (0) 4 (19.0)
MSU protrusion size classification 1 37 (92.5) 17 (58.6) 3 (14.3) 43.454 0.000
2 3 (7.5) 12 (41.4) 12 (57.1)
3 0 (0) 0 (0) 6 (28.6)
MSU protrusion location classification 1a 32 (80.0) 12 (41.4) 1 (4.8) 55.406 0.000
1b 4 (10.0) 5 (17.2) 2 (9.5)
1c 1 (2.5) 6 (20.7) 0 (0)
2a 3 (7.5) 5 (17.2) 3 (14.3)
2b 0 (0) 1 (3.4) 3 (14.3)
2ab 0 (0) 0 (0) 4 (19.0)
2c 0 (0) 0 (0) 2 (9.5)
3a 0 (0) 0 (0) 2 (9.5)
3b 0 (0) 0 (0) 1 (4.8)
3ab 0 (0) 0 (0) 3 (14.3)
Thickness of ligamentum flavum (cm) 0.31 ± 0.09 0.32 ± 0.10 0.35 ± 0.11 1.499 0.229
SI 0.23 ± 0.05 0.36 ± 0.04 0.53 ± 0.06 263.190 0.000
STI 0.15 ± 0.05 0.25 ± 0.06 0.42 ± 0.12 61.056 0.000
TI 0.78 ± 0.05 0.66 ± 0.05 0.48 ± 0.07 195.220 0.000
AN angle (°) 26.33 ± 4.27 14.87 ± 5.59 4.20 ± 3.86 162.481 0.000

The frequency statistics of Modic classification, Pfirrmann classification, MSU protrusion size classification, and MSU protrusion location classification between groups were expressed as (number [%]). Chi-square test was used.

The Modic classification, Pfirrmann classification, MSU protrusion size classification, and MSU protrusion location classification are subsequently pairwise multiple comparisons, which were conducted using the α division method. The SI, STI, TI, and AN angle underwent pairwise multiple comparisons utilizing the Bonferroni method. The results showed that there were statistically significant differences (adjusted P < 0.05) in pairwise comparisons of the Pfirrmann classification, MSU protrusion size classification, MSU protrusion location classification, SI, STI, TI, and AN angle between the mild, moderate, and severe groups. These variables were included in the correlation analysis. However, there was no statistically significant difference (adjusted P = 0.243 > 0.05) in the Modic classification between the moderate and severe groups, and it was excluded. Please refer to Table 3 for details.

Table 3

Multiple comparison of imaging features and statistical analysis of P value

Mild group vs moderate group Mild group vs severe group Moderate group vs severe group
Modic classification 0.015 0.000 0.243
Pfirrmann classification 0.011 0.000 0.000
MSU protrusion size classification 0.001 0.000 0.000
MSU protrusion location classification 0.002 0.000 0.002
SI 0.000 0.000 0.000
STI 0.000 0.000 0.004
TI 0.000 0.000 0.000
AN angle (°) 0.000 0.000 0.000

Since the ODI index did not follow a normal distribution (P > 0.05), the correlation analysis between statistically significant imaging features in pairwise comparisons and the ODI index was conducted using Spearman’s correlation. The results of the correlation analysis showed the following: the Pfirrmann classification correlation coefficient ( r s = 0.674, P = 0.000) (Figure 12), MSU protrusion size classification correlation coefficient ( r s = 0.688, P = 0.000) (Figure 13), MSU protrusion location classification correlation coefficient ( r s = 0.744, P = 0.000) (Figure 14), SI correlation coefficient ( r s = 0.885, P = 0.000) (Figure 15), and STI correlation coefficient ( r s = 0.796, P = 0.000) (Figure 16) were positively correlated with the degree of pain. TI correlation coefficient ( r s =−0.839, P = 0.000) (Figure 17) and AN Angle correlation coefficient ( r s = −0.848, P = 0.000) (Figure 18) were negatively correlated with the degree of pain. All imaging features showed significant correlations with the degree of pain, as shown in Table 4.

Figure 12 Correlation analysis between Pfirrmann classification and ODI.
Figure 12

Correlation analysis between Pfirrmann classification and ODI.

Figure 13 Correlation analysis between MSU protrusion size classification and ODI.
Figure 13

Correlation analysis between MSU protrusion size classification and ODI.

Figure 14 Correlation analysis between MSU protrusion location classification and ODI.
Figure 14

Correlation analysis between MSU protrusion location classification and ODI.

Figure 15 Correlation analysis between SI and ODI.
Figure 15

Correlation analysis between SI and ODI.

Figure 16 Correlation analysis between STI and ODI.
Figure 16

Correlation analysis between STI and ODI.

Figure 17 Correlation analysis between TI and ODI.
Figure 17

Correlation analysis between TI and ODI.

Figure 18 Correlation analysis between AN angle and ODI.
Figure 18

Correlation analysis between AN angle and ODI.

Table 4

Spearman rank correlation analysis

Pfirrmann classification MSU protrusion size classification MSU protrusion location classification SI STI TI AN angle (°)
Correlation coefficient 0.674** 0.688** 0.744** 0.885** 0.796** −0.839** −0.848**
P 0.000 0.000 0.000 0.000 0.000 0.000 0.000

** At the 0.01 level (two-way), correlation is significant.

After training, the value of loss function decreased significantly (Figure 19). In addition, after 1,000 rounds of testing, the visualization results of model specificity (95 ± 2%, mean value 95.63%), sensitivity (91 ± 2%, mean value 91.08%), and accuracy (91 ± 2%, mean value 91.03%) are shown in Figure 20.

Figure 19 BP neural network training loss function value drop plot.
Figure 19

BP neural network training loss function value drop plot.

Figure 20 Specificity, sensitivity, and accuracy of BP neural network model.
Figure 20

Specificity, sensitivity, and accuracy of BP neural network model.

The loss function of BP neural network model decreased, the model converged, gradually tended to be stable, and the recognition accuracy increased.

The higher the specificity, sensitivity, and accuracy, the better the performance of the model.

Under the condition of multi-classification, we evaluated the performance of the model, drew the receiver operating characteristic (ROC) curve of the model, and solved the area AUC under the ROC curve, as shown in Figure 21. The area of ROC curve of class 0 for predicting mild symptom group was 0.98 (P = 0.03). The area of ROC curve of class 1 for predicting moderate symptom group was 0.96 (P = 0.05). The area of ROC curve of class 2 for predicting severe symptom group was 1.00 (P = 0.01). The AUC under the ROC curve was ideal, which proved the success of the model training. The processing efficiency of the model network in this study showed that the average reasoning time of a single case was 50 ms.

Figure 21 Multi-class ROC curve.
Figure 21

Multi-class ROC curve.

ROC curve of class 1 was the ROC curve drawn with the first class as the positive class. Macro-average ROC curve and micro-average ROC curve were the ROC curves of macro average and micro average, respectively.

4 Discussion

In this study, the following imaging features were selected to evaluate the correlation between degenerative LDH and pain: Pfirrmann classification for assessing disc degeneration level, Modic classification for evaluating cartilage endplate signal changes, ligamentum flavum hypertrophy degree, and intraspinal occupation indicators (MSU regional localization, SI, STI, TI, and AN angle). These imaging features were chosen to eliminate the adverse influence of intervertebral canal variations among different patients.

In this study, the improved Pfirrmann grading system was used for IDD. The higher the grade, the more serious the degree of intervertebral disc degeneration. The results of this study demonstrated a significant correlation between disc degeneration and LDH pain symptoms, suggesting that the degree of disc degeneration generally reflects the distribution of LDH severity. The conclusion drawn is that the more severe the lumbar disc degeneration, the more significant the LDH pain symptoms. This finding is consistent with the study conducted by Faur et al. [33] on the correlation between lumbar disc degeneration and multifidus muscle (LMM) fat atrophy in patients with lower back pain, which found a higher rate of LMM atrophy in the L5/S1 segment and its correlation with disc degeneration. It is also in line with the research conducted by Foizer et al. [34], who discovered that chronic idiopathic lower back pain patients often have moderate to severe lumbar disc degeneration.

If the pressure on the endplate exceeds its limit, microfractures can occur in the subchondral bone trabeculae, leading to local changes in the spine and resulting in Modic changes. In 1987, de Roos et al. first reported changes in vertebral bone marrow signal near the endplate region in MRI images of patients with lumbar disc degeneration [35]. In 1988, Modic systematically proposed the classification of degenerative lesions in the vertebral endplate and subchondral bone signal changes on MRI, along with histological changes [24,25,26]. These changes include bone marrow edema, fatty degeneration, or sclerosis [36]. Modic changes can reflect the severity of clinical lumbar disc degeneration [37], and Kokkonen et al. also confirmed the association between endplate degeneration and lumbar disc degeneration, suggesting that endplate degeneration may be a result rather than a cause of disc degeneration [38]. In our study, out of the 90 cases, endplate changes were not observed in 60 cases. The limited number of positive results did not yield statistically significant differences in Modic classification between pairwise comparisons, thus the correlation between endplate degeneration and the severity of LDH symptoms could not be confirmed. This finding contradicts the results of studies suggesting a high diagnostic value of Modic changes for discogenic LBP [39]. However, a systematic review also found no association between Modic classification and LBP-related outcomes [40].

The ligamentum flavum is distributed between adjacent vertebral laminae and is rich in elastic fibers, making it tough and thick. Due to its close proximity to the dura mater, thickening of the ligamentum flavum can cause compression and irritation of the dura mater. In addition, most patients with ligamentum flavum hypertrophy will also experience symptoms of nerve root canal stenosis. Due to its high content of elastic fibers, the ligamentum flavum appears as a “V” shape on axial MRI images, allowing clear visualization of its thickening and deformation. This enables an accurate assessment of the degree of compression on the dura mater and the extent of spinal canal narrowing. However, in this study, statistical analysis revealed that there was no significant statistical difference observed in the thickness of the ligamentum flavum within the LDH pain group. Therefore, there is no evidence to support any correlation between ligamentum flavum hypertrophy and LDH symptoms.

MSU regional localization is an assessment criterion for evaluating the size and location of lumbar disc protrusions within and outside the spinal canal. The size of the protruded nucleus pulposus is categorized into three levels: 1, 2, and 3. The location of the protrusion is classified into four regions: A, B, AB, and C. From an anatomical perspective, regions B and C appear narrower than region A. Therefore, when the protruded disc is located in regions B or C, it usually indicates more severe compression of the spinal nerves. In this study, both the MSU protrusion size classification and the MSU protrusion location classification showed statistical significance among the three groups. Furthermore, both the MSU protrusion size classification and the MSU protrusion location classification were significantly correlated with pain symptom scores, indicating that the size and location of the protrusion are important imaging features for evaluating LDH symptoms.

The AN angle serves as an indicator of the proximity of the protruding nucleus pulposus to the intervertebral space and the level of compression exerted on the nerve root. The results of this study showed a significant correlation between the AN angle and the degree of pain, which is consistent with the findings of Kobayashi et al. [41]. Their findings indicated that nerve root compression led to reduced blood flow and neuronal cell count, consequently impacting neurotransmitter metabolism and resulting in impaired nerve function.

The study results demonstrated significant correlations between the following indicators of intraspinal occupancy: MSU regional localization (MSU protrusion size classification and MSU protrusion location classification), SI, STI, TI, AN angle, and LDH pain. These findings are consistent with Thapa et al.’s study [42], which analyzed the correlation between clinical characteristics and MRI-visible abnormal imaging features (such as disc protrusion type, location, nerve root, and intervertebral foramen damage) in LDH patients. Thapa found a good correlation between disc protrusion with significant compression of the nerve root and clinical features, supporting the theory of mechanical compression. The theory of mechanical compression is currently the most widely accepted view due to its directness. According to this theory, the protruded nucleus pulposus of the intervertebral disc directly or indirectly compresses the nerve root and/or the dura mater, affecting the normal blood supply to the nerve root and inducing symptoms of nerve root irritation. Mixter and Barr [43] first reported sciatic nerve pain associated with LDH, suggesting that it is caused by mechanical injury to the nerve root, with the main factors being indirect compression of the spinal nerve root and the tension effect on the nerve root. The symptoms are related to changes in the position of the protruded disc tissue, the transmission of pain information within the nervous system, and the interaction between the nucleus pulposus, dura mater, or nerve root. In addition, it may also be related to local microcirculation and inflammatory changes caused by protruded nucleus pulposus, as well as osteophyte degeneration and changes in lumbar stress posture. However, clinical studies have found that many asymptomatic individuals still show compressive changes in LDH imaging [44,45,46]. Whether the theory of mechanical compression alone can cause pain requires further investigation.

During the degenerative process of lumbar intervertebral discs, there is an increase in the number of senescent cells. It induces the upregulation of pro-inflammatory cytokines, chemokines, and tissue-damaging proteases, including tumor necrosis factor-alpha (TNF-α) and interleukin-1 alpha (IL-1α). This can induce local inflammation, leading to neurological signs such as radicular pain and exacerbating pain in patients with LDH [47]. Pain signals originating from the intervertebral disc can be transmitted through adjacent structures via peripheral nerve fibers. The interaction between nerve fibers and inflammatory mediators in the nucleus pulposus can trigger lower back pain. However, this study focuses primarily on the analysis of LDH imaging features and cannot confirm whether the pain associated with LDH is related to the chemical radiculitis theory.

In recent years, the development of machine learning has greatly enhanced the ability to handle complex and large amounts of information, particularly in the field of medical diagnosis. Numerous researchers have utilized machine learning techniques for in-depth automated grading and predictive diagnosis of diseases. For instance, Jamaludin trained a convolutional neural network to automatically grade lumbar intervertebral discs and vertebral bodies [48]. Abdel-Zaher and Eldeib designed a neural network, DBN-NN, for automatic diagnosis of breast cancer, achieving an overall accuracy of 99.68%, sensitivity of 100%, and specificity of 99.47% [49]. Kleesiek et al. conducted work on the classification of lung nodules, achieving an accuracy of 75.01% and sensitivity of 83.35%. In a 10-fold cross-validation, the false positive rate per patient was 0.39 [50]. Wei and Yang discovered that a back propagation artificial neural network model demonstrated higher predictive accuracy for non-invasive diagnosis of digestive system diseases compared to linear regression [51].

The BP neural network is a multi-layer feedforward neural network structure developed by McClelland and Rumelhart for supervised learning [52]. It uses a variety of training samples to modify the weights in order to minimize the error value. The error function is obtained through a recursive process of BP starting from the output layer. Therefore, it is based on the backpropagation algorithm and is also known as an error BP neural network.

The design of a multi-layer neural network enables the BP neural network to learn more feature information from the input, thus completing more complex classification tasks. It is suitable for simulating the approximate relationship between input and output in computational modeling. The network operation involves two steps: forward propagation and backward learning. During forward propagation, the input data passes through the hidden layers layer by layer from the input layer. During network training, optimization is performed in the direction that reduces the value of the loss function. the value of the loss function becomes smaller and smaller. In the reverse learning process, numerical adjustments are made from the output layer to the input layer to achieve the desired output of the network. With the deepening of the learning process of BP neural network, its performance in complex model fitting and distribution approximation is obviously superior to the traditional statistical methods. [53]. The BP neural network transforms linear data into nonlinear data through activation functions, making it capable of handling linearly inseparable problems, which aligns with the characteristics of the data in this study.

Prior literature research revealed lack of studies applying the BP neural network model to LDH research. Hence, developing an LDH pain prediction model based on the BP neural network holds significant importance for LDH diagnosis and pathogenesis investigation. In this study, we successfully developed and validated a BP neural network for automated LDH pain grading, which displayed impressive performance in pain classification. The study results indicated the BP neural network model’s convergence and approaching stability, as evidenced by the decreasing loss and increasing recognition accuracy. The BP neural network training model results showed the specificity (95 ± 2%, mean value: 95.63%), sensitivity (91 ± 2%, mean value: 91.08%), and accuracy (91 ± 2%, mean value: 91.03%) of the model. The area under the ROC curve for predicting pain type 0 (mild), type 1 (moderate), and type 2 (severe) were 0.98, 0.96, and 1.00, respectively. Compared to similar studies, the results of this study are at a relatively high level. The macro-average ROC curve and micro-average ROC curve are average indicators for multi-classification problems. The high values of both micro-average and macro-average (both above 95%) indicate good classification performance of the model in this study.

In this study, we meticulously weighed the pros and cons of the BP neural network and strategically employed suitable techniques to address its limitations. Consequently, the BP neural network’s performance reached an impressive level. Despite the small data size, the utilization of oversampling techniques, and k-fold cross-validation effectively mitigated overfitting concerns. This successful approach validated the BP model’s predictive classification capability for assessing the degree of LDH pain.

  1. Funding information: Study on the imaging intelligent evaluation method of acupuncture prescription behavior in degenerative spinal diseases: 2019KZDZX1039.

  2. Author contributions: X.Y.R.: manuscript writing, investigation, methodology, data analysis, and validation; H.W.L.: data collection, data curation, and draft editing; S.J.H.: technical support; X.W.: data collection; H.L.Z.: study conceptualization, supervision, reviewing, manuscript writing, revising, and editing.

  3. Conflict of interest: Authors state no conflict of interest.

  4. Data availability statement: The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.

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Received: 2023-04-29
Revised: 2023-07-04
Accepted: 2023-07-16
Published Online: 2023-09-08

© 2023 the author(s), published by De Gruyter

This work is licensed under the Creative Commons Attribution 4.0 International License.

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  266. Erratum
  267. Erratum to “Protein Z modulates the metastasis of lung adenocarcinoma cells”
  268. Erratum to “BRCA1 subcellular localization regulated by PI3K signaling pathway in triple-negative breast cancer MDA-MB-231 cells and hormone-sensitive T47D cells”
  269. Retraction
  270. Retraction to “Protocatechuic acid attenuates cerebral aneurysm formation and progression by inhibiting TNF-alpha/Nrf-2/NF-kB-mediated inflammatory mechanisms in experimental rats”
https://doi.org/10.1515/biol-2022-0673
Keywords for this article
lumbar disc herniation; magnetic resonance imaging; prediction model; imaging feature; pain score
Creative Commons
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  87. Berberine suppressed sarcopenia insulin resistance through SIRT1-mediated mitophagy
  88. DUSP2 inhibits the progression of lupus nephritis in mice by regulating the STAT3 pathway
  89. Lung abscess by Fusobacterium nucleatum and Streptococcus spp. co-infection by mNGS: A case series
  90. Genetic alterations of KRAS and TP53 in intrahepatic cholangiocarcinoma associated with poor prognosis
  91. Granulomatous polyangiitis involving the fourth ventricle: Report of a rare case and a literature review
  92. Studying infant mortality: A demographic analysis based on data mining models
  93. Metaplastic breast carcinoma with osseous differentiation: A report of a rare case and literature review
  94. Protein Z modulates the metastasis of lung adenocarcinoma cells
  95. Inhibition of pyroptosis and apoptosis by capsaicin protects against LPS-induced acute kidney injury through TRPV1/UCP2 axis in vitro
  96. TAK-242, a toll-like receptor 4 antagonist, against brain injury by alleviates autophagy and inflammation in rats
  97. Primary mediastinum Ewing’s sarcoma with pleural effusion: A case report and literature review
  98. Association of ADRB2 gene polymorphisms and intestinal microbiota in Chinese Han adolescents
  99. Tanshinone IIA alleviates chondrocyte apoptosis and extracellular matrix degeneration by inhibiting ferroptosis
  100. Study on the cytokines related to SARS-Cov-2 in testicular cells and the interaction network between cells based on scRNA-seq data
  101. Effect of periostin on bone metabolic and autophagy factors during tooth eruption in mice
  102. HP1 induces ferroptosis of renal tubular epithelial cells through NRF2 pathway in diabetic nephropathy
  103. Intravaginal estrogen management in postmenopausal patients with vaginal squamous intraepithelial lesions along with CO2 laser ablation: A retrospective study
  104. Hepatocellular carcinoma cell differentiation trajectory predicts immunotherapy, potential therapeutic drugs, and prognosis of patients
  105. Effects of physical exercise on biomarkers of oxidative stress in healthy subjects: A meta-analysis of randomized controlled trials
  106. Identification of lysosome-related genes in connection with prognosis and immune cell infiltration for drug candidates in head and neck cancer
  107. Development of an instrument-free and low-cost ELISA dot-blot test to detect antibodies against SARS-CoV-2
  108. Research progress on gas signal molecular therapy for Parkinson’s disease
  109. Adiponectin inhibits TGF-β1-induced skin fibroblast proliferation and phenotype transformation via the p38 MAPK signaling pathway
  110. The G protein-coupled receptor-related gene signatures for predicting prognosis and immunotherapy response in bladder urothelial carcinoma
  111. α-Fetoprotein contributes to the malignant biological properties of AFP-producing gastric cancer
  112. CXCL12/CXCR4/CXCR7 axis in placenta tissues of patients with placenta previa
  113. Association between thyroid stimulating hormone levels and papillary thyroid cancer risk: A meta-analysis
  114. Significance of sTREM-1 and sST2 combined diagnosis for sepsis detection and prognosis prediction
  115. Diagnostic value of serum neuroactive substances in the acute exacerbation of chronic obstructive pulmonary disease complicated with depression
  116. Research progress of AMP-activated protein kinase and cardiac aging
  117. TRIM29 knockdown prevented the colon cancer progression through decreasing the ubiquitination levels of KRT5
  118. Cross-talk between gut microbiota and liver steatosis: Complications and therapeutic target
  119. Metastasis from small cell lung cancer to ovary: A case report
  120. The early diagnosis and pathogenic mechanisms of sepsis-related acute kidney injury
  121. The effect of NK cell therapy on sepsis secondary to lung cancer: A case report
  122. Erianin alleviates collagen-induced arthritis in mice by inhibiting Th17 cell differentiation
  123. Loss of ACOX1 in clear cell renal cell carcinoma and its correlation with clinical features
  124. Signalling pathways in the osteogenic differentiation of periodontal ligament stem cells
  125. Crosstalk between lactic acid and immune regulation and its value in the diagnosis and treatment of liver failure
  126. Clinicopathological features and differential diagnosis of gastric pleomorphic giant cell carcinoma
  127. Traumatic brain injury and rTMS-ERPs: Case report and literature review
  128. Extracellular fibrin promotes non-small cell lung cancer progression through integrin β1/PTEN/AKT signaling
  129. Knockdown of DLK4 inhibits non-small cell lung cancer tumor growth by downregulating CKS2
  130. The co-expression pattern of VEGFR-2 with indicators related to proliferation, apoptosis, and differentiation of anagen hair follicles
  131. Inflammation-related signaling pathways in tendinopathy
  132. CD4+ T cell count in HIV/TB co-infection and co-occurrence with HL: Case report and literature review
  133. Clinical analysis of severe Chlamydia psittaci pneumonia: Case series study
  134. Bioinformatics analysis to identify potential biomarkers for the pulmonary artery hypertension associated with the basement membrane
  135. Influence of MTHFR polymorphism, alone or in combination with smoking and alcohol consumption, on cancer susceptibility
  136. Catharanthus roseus (L.) G. Don counteracts the ampicillin resistance in multiple antibiotic-resistant Staphylococcus aureus by downregulation of PBP2a synthesis
  137. Combination of a bronchogenic cyst in the thoracic spinal canal with chronic myelocytic leukemia
  138. Bacterial lipoprotein plays an important role in the macrophage autophagy and apoptosis induced by Salmonella typhimurium and Staphylococcus aureus
  139. TCL1A+ B cells predict prognosis in triple-negative breast cancer through integrative analysis of single-cell and bulk transcriptomic data
  140. Ezrin promotes esophageal squamous cell carcinoma progression via the Hippo signaling pathway
  141. Ferroptosis: A potential target of macrophages in plaque vulnerability
  142. Predicting pediatric Crohn's disease based on six mRNA-constructed risk signature using comprehensive bioinformatic approaches
  143. Applications of genetic code expansion and photosensitive UAAs in studying membrane proteins
  144. HK2 contributes to the proliferation, migration, and invasion of diffuse large B-cell lymphoma cells by enhancing the ERK1/2 signaling pathway
  145. IL-17 in osteoarthritis: A narrative review
  146. Circadian cycle and neuroinflammation
  147. Probiotic management and inflammatory factors as a novel treatment in cirrhosis: A systematic review and meta-analysis
  148. Hemorrhagic meningioma with pulmonary metastasis: Case report and literature review
  149. SPOP regulates the expression profiles and alternative splicing events in human hepatocytes
  150. Knockdown of SETD5 inhibited glycolysis and tumor growth in gastric cancer cells by down-regulating Akt signaling pathway
  151. PTX3 promotes IVIG resistance-induced endothelial injury in Kawasaki disease by regulating the NF-κB pathway
  152. Pancreatic ectopic thyroid tissue: A case report and analysis of literature
  153. The prognostic impact of body mass index on female breast cancer patients in underdeveloped regions of northern China differs by menopause status and tumor molecular subtype
  154. Report on a case of liver-originating malignant melanoma of unknown primary
  155. Case report: Herbal treatment of neutropenic enterocolitis after chemotherapy for breast cancer
  156. The fibroblast growth factor–Klotho axis at molecular level
  157. Characterization of amiodarone action on currents in hERG-T618 gain-of-function mutations
  158. A case report of diagnosis and dynamic monitoring of Listeria monocytogenes meningitis with NGS
  159. Effect of autologous platelet-rich plasma on new bone formation and viability of a Marburg bone graft
  160. Small breast epithelial mucin as a useful prognostic marker for breast cancer patients
  161. Continuous non-adherent culture promotes transdifferentiation of human adipose-derived stem cells into retinal lineage
  162. Nrf3 alleviates oxidative stress and promotes the survival of colon cancer cells by activating AKT/BCL-2 signal pathway
  163. Favorable response to surufatinib in a patient with necrolytic migratory erythema: A case report
  164. Case report of atypical undernutrition of hypoproteinemia type
  165. Down-regulation of COL1A1 inhibits tumor-associated fibroblast activation and mediates matrix remodeling in the tumor microenvironment of breast cancer
  166. Sarcoma protein kinase inhibition alleviates liver fibrosis by promoting hepatic stellate cells ferroptosis
  167. Research progress of serum eosinophil in chronic obstructive pulmonary disease and asthma
  168. Clinicopathological characteristics of co-existing or mixed colorectal cancer and neuroendocrine tumor: Report of five cases
  169. Role of menopausal hormone therapy in the prevention of postmenopausal osteoporosis
  170. Precisional detection of lymph node metastasis using tFCM in colorectal cancer
  171. Advances in diagnosis and treatment of perimenopausal syndrome
  172. A study of forensic genetics: ITO index distribution and kinship judgment between two individuals
  173. Acute lupus pneumonitis resembling miliary tuberculosis: A case-based review
  174. Plasma levels of CD36 and glutathione as biomarkers for ruptured intracranial aneurysm
  175. Fractalkine modulates pulmonary angiogenesis and tube formation by modulating CX3CR1 and growth factors in PVECs
  176. Novel risk prediction models for deep vein thrombosis after thoracotomy and thoracoscopic lung cancer resections, involving coagulation and immune function
  177. Exploring the diagnostic markers of essential tremor: A study based on machine learning algorithms
  178. Evaluation of effects of small-incision approach treatment on proximal tibia fracture by deep learning algorithm-based magnetic resonance imaging
  179. An online diagnosis method for cancer lesions based on intelligent imaging analysis
  180. Medical imaging in rheumatoid arthritis: A review on deep learning approach
  181. Predictive analytics in smart healthcare for child mortality prediction using a machine learning approach
  182. Utility of neutrophil–lymphocyte ratio and platelet–lymphocyte ratio in predicting acute-on-chronic liver failure survival
  183. A biomedical decision support system for meta-analysis of bilateral upper-limb training in stroke patients with hemiplegia
  184. TNF-α and IL-8 levels are positively correlated with hypobaric hypoxic pulmonary hypertension and pulmonary vascular remodeling in rats
  185. Stochastic gradient descent optimisation for convolutional neural network for medical image segmentation
  186. Comparison of the prognostic value of four different critical illness scores in patients with sepsis-induced coagulopathy
  187. Application and teaching of computer molecular simulation embedded technology and artificial intelligence in drug research and development
  188. Hepatobiliary surgery based on intelligent image segmentation technology
  189. Value of brain injury-related indicators based on neural network in the diagnosis of neonatal hypoxic-ischemic encephalopathy
  190. Analysis of early diagnosis methods for asymmetric dementia in brain MR images based on genetic medical technology
  191. Early diagnosis for the onset of peri-implantitis based on artificial neural network
  192. Clinical significance of the detection of serum IgG4 and IgG4/IgG ratio in patients with thyroid-associated ophthalmopathy
  193. Forecast of pain degree of lumbar disc herniation based on back propagation neural network
  194. SPA-UNet: A liver tumor segmentation network based on fused multi-scale features
  195. Systematic evaluation of clinical efficacy of CYP1B1 gene polymorphism in EGFR mutant non-small cell lung cancer observed by medical image
  196. Rehabilitation effect of intelligent rehabilitation training system on hemiplegic limb spasms after stroke
  197. A novel approach for minimising anti-aliasing effects in EEG data acquisition
  198. ErbB4 promotes M2 activation of macrophages in idiopathic pulmonary fibrosis
  199. Clinical role of CYP1B1 gene polymorphism in prediction of postoperative chemotherapy efficacy in NSCLC based on individualized health model
  200. Lung nodule segmentation via semi-residual multi-resolution neural networks
  201. Evaluation of brain nerve function in ICU patients with Delirium by deep learning algorithm-based resting state MRI
  202. A data mining technique for detecting malignant mesothelioma cancer using multiple regression analysis
  203. Markov model combined with MR diffusion tensor imaging for predicting the onset of Alzheimer’s disease
  204. Effectiveness of the treatment of depression associated with cancer and neuroimaging changes in depression-related brain regions in patients treated with the mediator-deuterium acupuncture method
  205. Molecular mechanism of colorectal cancer and screening of molecular markers based on bioinformatics analysis
  206. Monitoring and evaluation of anesthesia depth status data based on neuroscience
  207. Exploring the conformational dynamics and thermodynamics of EGFR S768I and G719X + S768I mutations in non-small cell lung cancer: An in silico approaches
  208. Optimised feature selection-driven convolutional neural network using gray level co-occurrence matrix for detection of cervical cancer
  209. Incidence of different pressure patterns of spinal cerebellar ataxia and analysis of imaging and genetic diagnosis
  210. Pathogenic bacteria and treatment resistance in older cardiovascular disease patients with lung infection and risk prediction model
  211. Adoption value of support vector machine algorithm-based computed tomography imaging in the diagnosis of secondary pulmonary fungal infections in patients with malignant hematological disorders
  212. From slides to insights: Harnessing deep learning for prognostic survival prediction in human colorectal cancer histology
  213. Ecology and Environmental Science
  214. Monitoring of hourly carbon dioxide concentration under different land use types in arid ecosystem
  215. Comparing the differences of prokaryotic microbial community between pit walls and bottom from Chinese liquor revealed by 16S rRNA gene sequencing
  216. Effects of cadmium stress on fruits germination and growth of two herbage species
  217. Bamboo charcoal affects soil properties and bacterial community in tea plantations
  218. Optimization of biogas potential using kinetic models, response surface methodology, and instrumental evidence for biodegradation of tannery fleshings during anaerobic digestion
  219. Understory vegetation diversity patterns of Platycladus orientalis and Pinus elliottii communities in Central and Southern China
  220. Studies on macrofungi diversity and discovery of new species of Abortiporus from Baotianman World Biosphere Reserve
  221. Food Science
  222. Effect of berrycactus fruit (Myrtillocactus geometrizans) on glutamate, glutamine, and GABA levels in the frontal cortex of rats fed with a high-fat diet
  223. Guesstimate of thymoquinone diversity in Nigella sativa L. genotypes and elite varieties collected from Indian states using HPTLC technique
  224. Analysis of bacterial community structure of Fuzhuan tea with different processing techniques
  225. Untargeted metabolomics reveals sour jujube kernel benefiting the nutritional value and flavor of Morchella esculenta
  226. Mycobiota in Slovak wine grapes: A case study from the small Carpathians wine region
  227. Elemental analysis of Fadogia ancylantha leaves used as a nutraceutical in Mashonaland West Province, Zimbabwe
  228. Microbiological transglutaminase: Biotechnological application in the food industry
  229. Influence of solvent-free extraction of fish oil from catfish (Clarias magur) heads using a Taguchi orthogonal array design: A qualitative and quantitative approach
  230. Chromatographic analysis of the chemical composition and anticancer activities of Curcuma longa extract cultivated in Palestine
  231. The potential for the use of leghemoglobin and plant ferritin as sources of iron
  232. Investigating the association between dietary patterns and glycemic control among children and adolescents with T1DM
  233. Bioengineering and Biotechnology
  234. Biocompatibility and osteointegration capability of β-TCP manufactured by stereolithography 3D printing: In vitro study
  235. Clinical characteristics and the prognosis of diabetic foot in Tibet: A single center, retrospective study
  236. Agriculture
  237. Biofertilizer and NPSB fertilizer application effects on nodulation and productivity of common bean (Phaseolus vulgaris L.) at Sodo Zuria, Southern Ethiopia
  238. On correlation between canopy vegetation and growth indexes of maize varieties with different nitrogen efficiencies
  239. Exopolysaccharides from Pseudomonas tolaasii inhibit the growth of Pleurotus ostreatus mycelia
  240. A transcriptomic evaluation of the mechanism of programmed cell death of the replaceable bud in Chinese chestnut
  241. Melatonin enhances salt tolerance in sorghum by modulating photosynthetic performance, osmoregulation, antioxidant defense, and ion homeostasis
  242. Effects of plant density on alfalfa (Medicago sativa L.) seed yield in western Heilongjiang areas
  243. Identification of rice leaf diseases and deficiency disorders using a novel DeepBatch technique
  244. Artificial intelligence and internet of things oriented sustainable precision farming: Towards modern agriculture
  245. Animal Sciences
  246. Effect of ketogenic diet on exercise tolerance and transcriptome of gastrocnemius in mice
  247. Combined analysis of mRNA–miRNA from testis tissue in Tibetan sheep with different FecB genotypes
  248. Isolation, identification, and drug resistance of a partially isolated bacterium from the gill of Siniperca chuatsi
  249. Tracking behavioral changes of confined sows from the first mating to the third parity
  250. The sequencing of the key genes and end products in the TLR4 signaling pathway from the kidney of Rana dybowskii exposed to Aeromonas hydrophila
  251. Development of a new candidate vaccine against piglet diarrhea caused by Escherichia coli
  252. Plant Sciences
  253. Crown and diameter structure of pure Pinus massoniana Lamb. forest in Hunan province, China
  254. Genetic evaluation and germplasm identification analysis on ITS2, trnL-F, and psbA-trnH of alfalfa varieties germplasm resources
  255. Tissue culture and rapid propagation technology for Gentiana rhodantha
  256. Effects of cadmium on the synthesis of active ingredients in Salvia miltiorrhiza
  257. Cloning and expression analysis of VrNAC13 gene in mung bean
  258. Chlorate-induced molecular floral transition revealed by transcriptomes
  259. Effects of warming and drought on growth and development of soybean in Hailun region
  260. Effects of different light conditions on transient expression and biomass in Nicotiana benthamiana leaves
  261. Comparative analysis of the rhizosphere microbiome and medicinally active ingredients of Atractylodes lancea from different geographical origins
  262. Distinguish Dianthus species or varieties based on chloroplast genomes
  263. Comparative transcriptomes reveal molecular mechanisms of apple blossoms of different tolerance genotypes to chilling injury
  264. Study on fresh processing key technology and quality influence of Cut Ophiopogonis Radix based on multi-index evaluation
  265. An advanced approach for fig leaf disease detection and classification: Leveraging image processing and enhanced support vector machine methodology
  266. Erratum
  267. Erratum to “Protein Z modulates the metastasis of lung adenocarcinoma cells”
  268. Erratum to “BRCA1 subcellular localization regulated by PI3K signaling pathway in triple-negative breast cancer MDA-MB-231 cells and hormone-sensitive T47D cells”
  269. Retraction
  270. Retraction to “Protocatechuic acid attenuates cerebral aneurysm formation and progression by inhibiting TNF-alpha/Nrf-2/NF-kB-mediated inflammatory mechanisms in experimental rats”
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