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Hepatobiliary surgery based on intelligent image segmentation technology

  • Fuchuan Wang , Chaohui Xiao , Tianye Jia , Liru Pan , Fengxia Du and Zhaohai Wang EMAIL logo
Published/Copyright: August 30, 2023
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Open Life Sciences
From the journal Open Life Sciences Volume 18 Issue 1

Abstract

Liver disease is an important disease that seriously threatens human health. It accounts for the highest proportion in various malignant tumors, and its incidence rate and mortality are on the rise, seriously affecting human health. Modern imaging has developed rapidly, but the application of image segmentation in liver tumor surgery is still rare. The application of image processing technology represented by artificial intelligence (AI) in surgery can greatly improve the efficiency of surgery, reduce surgical complications, and reduce the cost of surgery. Hepatocellular carcinoma is the most common malignant tumor in the world, and its mortality is second only to lung cancer. The resection rate of liver cancer surgery is high, and it is a multidisciplinary surgery, so it is necessary to explore the possibility of effective switching between different disciplines. Resection of hepatobiliary and pancreatic tumors is one of the most challenging and lethal surgical procedures. The operation requires a high level of doctors’ experience and understanding of anatomical structures. The surgical segmentation is slow and there may be obvious complications. Therefore, the surgical system needs to make full use of the relevant functions of AI technology and computer vision analysis software, and combine the processing strategy based on image processing algorithm and computer vision analysis model. Intelligent optimization algorithm, also known as modern heuristic algorithm, is an algorithm with global optimization performance, strong universality, and suitable for parallel processing. This algorithm generally has a strict theoretical basis, rather than relying solely on expert experience. In theory, the optimal solution or approximate optimal solution can be found in a certain time. This work studies the hepatobiliary surgery through intelligent image segmentation technology, and analyzes them through intelligent optimization algorithm. The research results showed that when other conditions were the same, there were three patients who had adverse reactions in hepatobiliary surgery through intelligent image segmentation technology, accounting for 10%. The number of patients with adverse reactions in hepatobiliary surgery by conventional methods was nine, accounting for 30%, which was significantly higher than the former, indicating a positive relationship between intelligent image segmentation technology and hepatobiliary surgery.

Keywords: hepatological surgery; image segmentation technology; artificial intelligence; postoperative adverse reactions; intelligent optimization algorithm

1 Introduction

With the development of science and technology, and the continuous improvement in medical image processing technology, how to classify various surgical procedures has become an important problem faced by surgeons. The most complex type of operation is hepatobiliary surgery, therefore, surgeons need to have rich clinical experience to be competent to block various complex organs in surgery. The operation requires the doctor to have a strong preoperative inspection ability, and to accurately locate the operation site, so as to accurately segment organs. In this process, there may be subtle differences between organs in different regions, so the division of each organ is very important.

Preoperative examination requires multiple manual operations to ensure clear images. In the process of operation, due to the lack of signal transmission between organ regions, doctors are often unable to quickly determine important organs and accurately grasp the surgical boundary, thus affecting the surgical effect. In addition, it may cause injury to patients due to incorrect operation. Therefore, it is of great significance to complete the operation efficiently and patient rehabilitation accurately and in a timely manner. In this process, image segmentation is one of the surgical steps, so it must be well and effectively applied to reduce surgical errors. Intelligent image segmentation technology has been applied in the field of intelligent medicine, especially in the medical field.

Based on this, this work studies hepatobiliary surgery based on intelligent image segmentation technology, and uses scientific methods to analyze it, to verify the relationship between the two. The innovation of this work was to combine intelligent image segmentation technology with liver surgery, which is not only a new attempt, but also in line with the development trend of modern medicine. This study not only has a novel perspective but also lays a foundation for liver surgery based on image segmentation technology, and provides new ideas and means for the treatment of liver tumors.

2 Related work

With the development of clinical work, the improvement in the diagnostic level of hepatobiliary diseases has laid a good foundation for hepatectomy technology and achieved great results in surgical operations. Wang et al’s. research provided reference for the application of indocyanine green fluorescence technology in hepatobiliary surgery [1]. Tang et al. found that in hepatobiliary surgery, augmented reality (AR) technology can help surgeons visualize the intrahepatic structure, so as to accurately operate and improve clinical results [2]. Mahdy et al’s. research showed that the infusion of terlipressin during hepatobiliary surgery can improve the portal vein hemodynamics during surgery, thereby reducing blood loss [3]. However, the above research works were carried out through the analysis of indocyanine green fluorescence technology and AR technology, lacking the research on image segmentation technology.

In view of the above problems, using intelligent image segmentation technology to analyze hepatobiliary surgery has become the research object of more and more scholars. Perica and Sun showed that the 3D printed liver model showed the liver anatomy and tumor with high accuracy, which can help preoperative planning and can be used to simulate the surgical process of treating malignant liver tumors [4]. Li et al. proposed a new semi supervised method for medical image segmentation and used it for liver segmentation of computed tomography (CT) scanning of liver tumor segmentation challenge dataset [5]. All these studies illustrate the applicability of image technology in the medical field, and provide rationality for the application of intelligent image segmentation technology to hepatobiliary surgery.

3 Construction of intelligent image segmentation technology

3.1 Overview of intelligent image segmentation technology

Intelligent image segmentation technology is an imaging technology that uses computer vision technology to extract, analyze, classify, retrieve, and mark images. With the development of artificial intelligence (AI) technology, AI has been widely used in the medical field, and image segmentation technology has broad application prospects in many fields. Computer vision in vision technology refers to the application of computer vision in various image processing fields, so as to realize the automatic recognition of various objects in images. At present, the most widely used and mature vision segmentation method in clinical application is based on regular neural network and machine learning algorithm, which can precisely locate the organs and tissues around the tumor. However, for liver tumors, the most widely used method is image segmentation algorithm based on depth learning. Intelligent image segmentation technology combines the advantages of machine learning method and deep learning technology, making it fast, accurate, less delay, interpretable, safe, and so on.

3.1.1 Introduction to image segmentation

Image segmentation is the core of the whole image processing, which is directly related to the subsequent processing. Image segmentation is the technology and process of dividing an image into several specific regions with unique properties and proposing interested objects. It is a key step from image processing to image analysis [6,7]. Image segmentation can be defined as dividing an image into similar areas that do not overlap each other. The area here can be regarded as a connected set of pixels, that is, a set of pixels where all pixels are adjacent or in contact. Image segmentation has been widely used in various fields, as shown in Figure 1.

Figure 1 Application of image segmentation.
Figure 1

Application of image segmentation.

3.1.2 Medical image segmentation technology

Medical image segmentation can be divided into three categories: manual segmentation, semi-automatic segmentation, and fully automatic segmentation. Manual segmentation mainly depends on the experience of doctors. In large-scale slice sequences, this method has high accuracy, but it requires a lot of work and time. The semi-automatic segmentation is realized by using the man–machine interface [8]. Automatic image segmentation is mainly realized by using fuzzy theory, neural network, and other related technologies.

The methods of medical image segmentation are divided into different categories, as shown in Figure 2.

Figure 2 Method division of medical image segmentation.
Figure 2

Method division of medical image segmentation.

The main applications of medical image segmentation are as follows: As the basis of image 3D reconstruction, 3D reconstruction algorithms or reconstruction software are used to achieve the reconstruction of three-dimensional structures. The statistics of characteristic parameters can be conducted to guide clinical diagnosis. Image compression is available for easy storage and management. The imaging mechanism of medical images is very complex, and its characteristics are fuzziness and non-uniformity. For example, the anatomy of the liver is similar to the gray scale of the surrounding tissues. When it is separated, there would be great adhesion, and it is difficult to separate the liver from the background [9,10]. Therefore, this study must recognize the technology and methods of medical image segmentation, use intelligent optimization algorithm to analyze the threshold of image segmentation technology, and segment the liver and gallbladder from the background [11,12].

3.1.3 Principle and method of intelligent segmentation technology

Computer vision is a science that studies how to make a machine “see.” Furthermore, it refers to the use of cameras and computers to replace human eyes for machine vision such as target recognition, tracking, and measurement, and further graphic processing to make computer processing more suitable for human eyes to observe or transmit images to instruments for detection [13]. The intelligent segmentation system is composed of image acquisition system, computer vision analysis software, and computer vision analysis model. CT means computerized tomography. It uses accurately collimated X-ray beam, γ ray, ultrasonic wave, etc., to scan one section after another around a certain part of the human body together with a highly sensitive detector. It has the characteristics of fast scanning time, clear images, etc., and can be used for the inspection of various diseases [14]. It can be divided into X-ray CT (X-CT) and γ ray CT (γ-CT) according to different rays used. First, CT images of patients were collected, including head plain scan and whole-body plain scan, and the lesions were diagnosed, classified, and resected by image processing algorithms and computer vision analysis model processing methods. The computer vision analysis model uses machine learning algorithm to extract image features and train the model. Image segmentation includes edge detection and feature extraction. The purpose of edge detection is to determine the edge and contour of the image (such as lesions and boundaries) [15,16]. Feature extraction can extract tumor edges and features at different scales (including edge segmentation) from the sample set. In the algorithm design, both processing space and image processing algorithm shall be taken into account to process local features, which can effectively improve the information required for segmentation of liver tumor tissue [17,18]. CT images can be used to perform cluster analysis from the perspective of the patient’s body to predict the patient’s focus type and tumor morphology, and then different features can be manually selected for further segmentation [19,20]. For example, when it is determined that the liver has a tumor, the ratio of tumor volume to tumor length can be used to judge the prognosis. When the tumor is located in the left lobe of the liver, intelligent segmentation is required. The tumor growing in the central area of the liver needs to be accurately segmented. However, there are some problems such as low segmentation accuracy, unsatisfactory segmentation, and possible errors when segmenting the whole. During prediction, the predicted image shall be segmented after being detected. In addition, due to the special location of hepatic portal vein, classification and extraction are required to ensure the accuracy and efficiency of clinical surgery. The combination of AI technology and computer vision analysis software designed to improve classification accuracy and save operation time can greatly improve its efficiency and accuracy.

3.2 Application of intelligent optimization algorithm in image threshold segmentation

3.2.1 Basic concepts of intelligent optimization algorithm

Intelligent optimization algorithm is a calculation method based on the social behavior of biological groups or the laws of natural phenomena. It is a random search algorithm based on bionics (biological intelligence) and simulation (physical phenomena). The optimal problem is a mathematical problem that finds the optimal solution from a large number of candidate solutions under the condition of meeting specific constraints. Mechanical design is to conceive, analyze, and calculate the working principle, structure, motion mode, transmission mode of force and energy, material, shape and size of each part, lubrication method, etc., of the machine according to the use requirements, and convert them into specific descriptions as the working process of manufacturing basis. Optimization problems generally exist in many fields of scientific research, such as enterprise production, signal processing, image processing, social management, automatic control, and mechanical design. At present, many optimization methods have been widely used in many aspects and have achieved significant economic and social benefits. The optimization method optimizes the problem-solving efficiency, and cost and resource allocation, and the larger the optimization problem is, the more obvious its impact would be.

Engineering technology refers to practical engineering technology, also known as production technology, which is actually applied in industrial production. With the development of theory and engineering technology, many complex optimization problems are characterized by large-scale, nonlinear, high complexity, and multi extremum. In addition, many problems require real-time computing, which makes it difficult for traditional optimization algorithms to meet the actual needs. In order to solve this problem, finding an effective optimal solution has become an important topic in various fields.

Whether it is bionic or imitative, it reflects the concept of “learning from nature.” This optimal method is called “natural operation.”

3.2.2 Basic principle of threshold segmentation

Threshold, also called critical value, refers to the lowest or highest value that an effect can produce. The basic idea of image threshold segmentation technology is to set a feature threshold according to certain standards, and divide each pixel in the image into corresponding regions. There are similar characteristics in each region, while there are significant differences between each region.

Assume that the size of the original image is N × M and the gray scale sequence is N . Then, the pixel gray value with ( a , b ) coordinate can be represented by the two-dimensional function O ( a , b ) , here a ∈ [ 1 , N ] , b ∈ [ 1 , M ] , 0 ≤ O ( a , b ) ≤ Z − 1 . Single threshold segmentation refers to dividing the whole image into two regions through the given feature threshold U and expressing them as follows:

(1) H ( a , b ) = y 0 , O ( a , b ) < U y 1 , O ( a , b ) ≥ U .

In a gray image, the gray value of each pixel is compared with the set threshold value. The result shows that when the gray value is higher than the threshold value, the pixel is white, and when the gray value is lower than the threshold value, the pixel is black.

The single threshold segmentation method is used to segment a single object. If there are multiple objects and these objects are in different gray levels, the multi threshold method is used to separate them. Assuming that the selected threshold is m , the segmented image can be expressed as follows:

(2) H ( a , b ) = z 0 , 0 < O ( a , b ) ≤ u 1 z 1 , u 1 < O ( a , b ) ≤ u 2 ⋮ , ⋮ z m , u m < O ( a , b ) ≤ Z − 1 .

Among them, z 0 , z 1 , … , z m and u 0 , u 1 , … , u m represent the different regions generated after image segmentation and the selected segmentation threshold, respectively.

3.2.3 Maximum entropy method

If the gray level of an image is between [ 0 , Z − 1 ] , the possibility of having o pixels in the image is q o . Then, the entropy of the image can be expressed using formula (3).

(3) J = − ∑ o = 1 Z − 1 ( q o ) lg ( q o ) .

The maximum entropy method is the most widely used threshold segmentation method at present. It assumes that the target area and the background area obey different probability distributions, and determines the segmentation threshold according to the maximum entropy. In the one-dimensional histogram, it is assumed that the u threshold is used to segment the image, and the entropy values of the target and background area are J 0 and J 1 , respectively

(4) J 0 = − ∑ o = 1 u − 1 q o e 0 lg q o e 0 ,

(5) J 1 = − ∑ o = 1 Z − 1 q o e 1 lg q o e 1 .

In this case, the entropy of the whole image is as follows:

(6) J = J 0 + J 1 .

The best segmentation threshold u ∗ selected should conform to the following:

(7) u ∗ = arg max 0 ≤ u ≤ Z − 1 { J ( u ) } .

3.3 General flow of intelligent optimization algorithm for image threshold segmentation

When solving the practical optimal problem, the intelligent optimization algorithm should first establish an accurate mathematical model and determine the objective function of the problem to be optimized. Second, the objective function is transformed into the adaptive function in the intelligent optimization algorithm. Finally, intelligent optimization methods can be used to find the optimal adaptability and corresponding optimal solution, and apply them to the actual optimization problems, as shown in Figure 3.

Figure 3 The flow of intelligent optimization algorithm in solving practical optimal problems.
Figure 3

The flow of intelligent optimization algorithm in solving practical optimal problems.

4 Experimental study on hepatobiliary surgery based on intelligent image segmentation technology

4.1 Preparation before experiment

4.1.1 Determination of experimental objects

From January 2021 to April 2021, a total of 60 patients undergoing hepatobiliary surgery were studied experimentally.

Inclusion criteria are as follows: male and female, age 50–75, American Society of Anesthesiologists (ASA) grade I or II, height 160–175 cm, weight 55–65 kg, no other diseases. ASA is a physician association organization engaged in education, research, and scientific research, aiming to improve and maintain the anesthesia related standards in the medical industry and improve the monitoring of patients.

Hepatocellular carcinoma (HCC): It is the most common type of carcinoma. HCC can be divided into primary and secondary types clinically, of which primary HCC is the most common malignant tumor of the liver. The liver is located in the front of the abdomen of the human body, which is spherical or oval in shape. Its epithelial cells would spread around after being damaged, so the mortality of liver cancer is very high.

  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 authors' institutional review board or equivalent committee.

4.1.2 Grouping of subjects

In this study, 60 patients were divided into 2 groups by stratified random method, 30 patients in each group. The experiment was divided into experimental group and control group. The hepatobiliary surgery was performed on the experimental group based on intelligent image segmentation technology (Group X), while the same was performed on the control group based on conventional methods (Group Y).

4.1.3 Experimental observation index

This study made detailed statistics on age, sex, weight, operation time, stay time in the anesthesia recovery room, first time out of bed after operation, length of hospital stay, occurrence and number of adverse events after operation, postoperative pain score, patient satisfaction, etc.

4.1.4 Experimental scoring standard

4.1.4.1 Visual analysis scale (VAS) scoring standard

All patients were followed up according to the corresponding time points, and the pain situation of 60 patients during the rehabilitation process was counted, and VAS score was taken as the standard. The VAS score is as follows:

0 point: no pain; 1–3 points: mild pain, which would not affect sleep; 4–6 points: moderate pain, which affects sleep and tolerable; 7–10: severe pain, unbearable, and would seriously affect appetite and sleep.

4.1.4.2 Patient satisfaction evaluation

The score is divided into five grades, A–E, of which A is very dissatisfied; B is not satisfied; C is satisfied; D is quite satisfied; E is extremely satisfied.

4.1.5 Experimental statistical treatment

In this study, Statistical Product Service Solutions and GraphPad Prism are used for statistical analysis. All tests are bilateral. The size of P value is used to determine whether it is statistically significant, that is, whether P value is less than 0.05.

4.2 Experimental results

4.2.1 General information of the patient

The age, weight, operation time, and sex of the two groups of patients are compared, and the results are shown in Table 1.

Table 1

Comparison of general conditions of two groups of patients

Index Grouping P value
X Y
Age 65.30 ± 8.16 65.50 ± 7.65 0.842
Weight (kg) 60.70 ± 12.1 61.20 ± 11.32 0.718
Operation time (min) 200 ± 20.54 230 ± 18.68 0.979
Gender (male/female) 16/14 15/15 0.944

Table 1 shows that the P values of the two groups in terms of age, weight, operation time, and gender are 0.842, 0.718, 0.979, and 0.944, respectively. P values were significantly greater than 0.05, indicating that there was no statistically significant difference between the two groups of patients, which was comparable.

4.2.2 Postoperative condition of the patient

The time of stay in the anesthesia recovery room, the time of first getting out of bed after surgery, and the number of days in the hospital are compared between the two groups of patients. The results are shown in Figures 4 and 5.

Figure 4 Time comparison of patients in group Y under three conditions. (a) Stay time in the anesthesia recovery room of group Y. (b) First time out of bed after operation in group Y, and (c) hospitalization days in group Y.
Figure 4

Time comparison of patients in group Y under three conditions. (a) Stay time in the anesthesia recovery room of group Y. (b) First time out of bed after operation in group Y, and (c) hospitalization days in group Y.

Figure 5 Time comparison of patients in Group X under three conditions. (a) Time of stay in the anesthesia recovery room of group X. (b) Time of getting out of bed for the first time after operation in group X and (c) hospitalization days in group X.
Figure 5

Time comparison of patients in Group X under three conditions. (a) Time of stay in the anesthesia recovery room of group X. (b) Time of getting out of bed for the first time after operation in group X and (c) hospitalization days in group X.

It can be seen from Figure 4(a) that 2 patients in Group Y stayed in the anesthesia recovery room for 6 h, accounting for 6.7%; 10 patients stayed for 6–12 h, accounting for 33.3%. There are 15 people from 12 to 24 h, accounting for 50%, and 3 people for more than 24 h, accounting for 10%. It can be seen from Figure 4(b) that the number of patients in Group Y who first got out of bed within 2 days after surgery is 0, and the number of patients in 2–4 days is 8, accounting for 26.7%. There are 18 people in 5–7 days, accounting for 60%, and 4 people in more than 7 days, accounting for 13.3%. It can be seen from Figure 4(c) that the number of patients in Group Y with hospitalization days of 10–15 days is 1, accounting for 3.3%, and the number of patients in 16–20 days is 9, accounting for 30%. The number of people in 21 to 25 days is 15, accounting for 50%, and the number of people in more than 25 days is 5, accounting for 16.7%. It can be seen from Figure 4 that most patients undergoing routine hepatobiliary surgery recover from anesthesia within 6–24 h, get out of bed for the first time within 2–7 days, and stay in hospital for 16–25 days.

It can be seen from Figure 5(a) that 11 patients in Group X stayed in the anesthesia recovery room for 6 h, accounting for 36.7%, and 14 patients stayed for 6–12 h, accounting for 46.7%. There were 4 persons in 12–24 h, accounting for 13.3%, and 1 person for more than 24 h, accounting for 3.3%. It can be seen from Figure 5(b) that the number of patients in Group X who first got out of bed within 2 days after surgery is 12, accounting for 40%. The number of people in 2–4 days is 15, accounting for 50%. The number of people in 5–7 days is 3, accounting for 10%, and there were no patients in the more than 7 days category. It can be seen from Figure 5(c) that the number of patients in Group X with hospitalization days of 10–15 days is 15, accounting for 50%, and the number of patients in 16–20 days is 13, accounting for 43.3%. There are 2 people from 21 to 25 days, accounting for 6.7%, and nobody in the more than 25 days group. It can be seen from Figure 5 that most patients undergoing hepatobiliary surgery based on intelligent image segmentation technology recovered from anesthesia within 12 h, got out of bed for the first time within 4 days, and stayed in hospital for 10–20 days.

To sum up, the hepatobiliary surgery based on intelligent image segmentation technology is obviously superior to the conventional hepatobiliary surgery for the duration of the patient’s stay in the anesthesia recovery room, the first time to get out of bed after surgery, and the number of days in hospital.

4.2.3 Comparison of VAS scores between two groups

Because the rest of the patient is also helpful for the recovery of the body, it is necessary to ensure that the patient has enough sleep after the operation. For this reason, this study analyzed the statistics on the rest of the two groups of patients after surgery, and obtained the results using VAS scores, as shown in Figure 6.

Figure 6 VAS pain scores of patients in both groups at rest. (a) Pain of patients in group X and (b) pain of patients in group Y.
Figure 6

VAS pain scores of patients in both groups at rest. (a) Pain of patients in group X and (b) pain of patients in group Y.

It can be seen from Figure 6(a) that 10 people in Group X scored 0 in VAS, accounting for 33.3%, and 14 people scored 1–3, accounting for 46.7%. Five people scored 4–6, accounting for 16.7%, and one person scored 7–10, accounting for 3.3%. It can be seen from Figure 6(b) that there are 2 persons in Group Y with VAS score of 0, accounting for 6.7%, and 4 persons with VAS score of 1–3, accounting for 13.3%. There are 6 persons with 4–6 points, accounting for 20%, and 18 persons with 7–10 points, accounting for 60%. It can be seen from Figure 6 that, according to the analysis of patients’ sleep quality, the number of patients with good sleep quality in Group X is significantly more than that in Group Y. It is more beneficial for patients’ health to perform hepatobiliary surgery with intelligent image segmentation technology than with conventional methods.

4.2.4 Comparison of patient satisfaction evaluation between two groups after operation

The satisfaction of the two groups of patients with their respective surgical results was investigated and counted, as shown in Figure 7.

Figure 7 Satisfaction evaluation of two groups of patients. (a) Satisfaction evaluation of group X and (b) satisfaction evaluation of group Y.
Figure 7

Satisfaction evaluation of two groups of patients. (a) Satisfaction evaluation of group X and (b) satisfaction evaluation of group Y.

It can be seen from Figure 7(a) that there are two people in Group X who are dissatisfied with the surgical results, accounting for 6.7%, and nine people are both satisfied and quite satisfied, accounting for 30%. The most satisfied patients were 10, accounting for 33.3%. There were no very dissatisfied patients. It can be seen from Figure 7(b) that eight people in Group Y were very dissatisfied with the surgical results, accounting for 26.7%. ten people were dissatisfied, accounting for 33.3%; six people were relatively satisfied, accounting for 20%. Five people were satisfied, accounting for 16.7%, and one person was very satisfied, accounting for 3.3%. It can be seen from Figure 7 that the satisfaction evaluation of patients in Group X on the surgical results is very good, while the satisfaction evaluation of patients in Group Y on the surgical results is not satisfactory, which further shows that the intelligent image segmentation technology is helpful for hepatobiliary surgery.

4.2.5 Comparison of adverse reactions after operation

In order to explain the relationship between intelligent image segmentation technology and hepatobiliary surgery more scientifically, this study analyzed the statistics on the adverse reactions and the number of patients after surgery. The results are shown in Figure 8.

Figure 8 Postoperative adverse reactions. (a) Number of adverse reactions in group X and (b) number of adverse reactions in group Y.
Figure 8

Postoperative adverse reactions. (a) Number of adverse reactions in group X and (b) number of adverse reactions in group Y.

It can be seen from Figure 8(a) that 1 case of nausea, vomiting, hypotension, and respiratory depression occurred in adverse reactions of Group X, accounting for 3.3%. It can be seen from Figure 8(b) that the number of nausea, vomiting, hypotension, and respiratory depression in adverse reactions of group Y was 4, 3, and 2, respectively, accounting for 13.3, 10, and 6.7%. It can be seen from Figure 8 that none of the patients had hematoma or infection, but other adverse reactions are obviously different. The number of adverse reactions in group X was three, accounting for 10%. The number of adverse reactions in group Y was nine, accounting for 30%. This shows that intelligent image segmentation technology can reduce the probability of bad reactions for patients after surgery.

5 Conclusion

The application of intelligent image segmentation technology is helpful to solve the practical problems in the accurate classification of liver tumors. At present, the doctors of liver surgery are highly demanding, the manual operation takes a long time, the patients are injured, and have many complications. The patients recovered slowly and had poor quality of life. AI image segmentation technology has the characteristics of high intelligence, which can quickly and effectively segment liver tissue accurately and effectively. It has high research value on the morphological characteristics and anatomical structure of liver cells. Intelligent CT has a very good development prospect. It can realize unattended operation, accumulate relevant knowledge and experience through learning, and solve practical problems through real-time surgery. In addition, image segmentation technology based on AI can effectively solve many difficult problems in the traditional surgery process, which plays a key role in the success of surgery. In other words, the application prospect of AI-based image segmentation technology in the diagnosis and treatment of liver diseases is broad, which can effectively improve the success rate of liver surgery and the therapeutic effect, and provide strong support and help for the diagnosis and treatment of liver and biliary surgery diseases in China. Due to the lack of comprehensive personal knowledge and ability, this work only studied 60 patients in a university affiliated hospital, and the number of experimental subjects was small. Although it is representative to some extent, it would still make the conclusion of the article questionable, which is not conducive to the development of intelligent image segmentation technology in liver surgery. It is hoped that more people and teams would join in this research in the future to expand the research objects and make the experimental conclusions more scientific.

  1. Funding information: Authors state no funding involved.

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

  3. 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: 2023-05-18
Revised: 2023-07-01
Accepted: 2023-07-12
Published Online: 2023-08-30

© 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-2022-0674
Keywords for this article
hepatological surgery; image segmentation technology; artificial intelligence; postoperative adverse reactions; intelligent optimization algorithm
Creative Commons
BY 4.0

Articles in the same Issue

  1. Biomedical Sciences
  2. Systemic investigation of inetetamab in combination with small molecules to treat HER2-overexpressing breast and gastric cancers
  3. Immunosuppressive treatment for idiopathic membranous nephropathy: An updated network meta-analysis
  4. Identifying two pathogenic variants in a patient with pigmented paravenous retinochoroidal atrophy
  5. Effects of phytoestrogens combined with cold stress on sperm parameters and testicular proteomics in rats
  6. A case of pulmonary embolism with bad warfarin anticoagulant effects caused by E. coli infection
  7. Neutrophilia with subclinical Cushing’s disease: A case report and literature review
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  9. Immunoregulation of synovial macrophages for the treatment of osteoarthritis
  10. Novel CPLANE1 c.8948dupT (p.P2984Tfs*7) variant in a child patient with Joubert syndrome
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  13. High glucose and high lipid induced mitochondrial dysfunction in JEG-3 cells through oxidative stress
  14. Pharmacological inhibition of the ubiquitin-specific protease 8 effectively suppresses glioblastoma cell growth
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  19. Long noncoding RNA ACART knockdown decreases 3T3-L1 preadipocyte proliferation and differentiation
  20. Surgery, adjuvant immunotherapy plus chemotherapy and radiotherapy for primary malignant melanoma of the parotid gland (PGMM): A case report
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  26. Experimental study on the optimization of ANM33 release in foam cells
  27. Primary retroperitoneal angiosarcoma: A case report
  28. Metabolomic analysis-identified 2-hydroxybutyric acid might be a key metabolite of severe preeclampsia
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  30. Effect of spaceflight on the phenotype and proteome of Escherichia coli
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  35. Triple-negative mouse breast cancer initiating cells show high expression of beta1 integrin and increased malignant features
  36. mNGS facilitates the accurate diagnosis and antibiotic treatment of suspicious critical CNS infection in real practice: A retrospective study
  37. The apatinib and pemetrexed combination has antitumor and antiangiogenic effects against NSCLC
  38. Radiotherapy for primary thyroid adenoid cystic carcinoma
  39. Design and functional preliminary investigation of recombinant antigen EgG1Y162–EgG1Y162 against Echinococcus granulosus
  40. Effects of losartan in patients with NAFLD: A meta-analysis of randomized controlled trial
  41. Bibliometric analysis of METTL3: Current perspectives, highlights, and trending topics
  42. Performance comparison of three scaling algorithms in NMR-based metabolomics analysis
  43. PI3K/AKT/mTOR pathway and its related molecules participate in PROK1 silence-induced anti-tumor effects on pancreatic cancer
  44. The altered expression of cytoskeletal and synaptic remodeling proteins during epilepsy
  45. Effects of pegylated recombinant human granulocyte colony-stimulating factor on lymphocytes and white blood cells of patients with malignant tumor
  46. Prostatitis as initial manifestation of Chlamydia psittaci pneumonia diagnosed by metagenome next-generation sequencing: A case report
  47. NUDT21 relieves sevoflurane-induced neurological damage in rats by down-regulating LIMK2
  48. Association of interleukin-10 rs1800896, rs1800872, and interleukin-6 rs1800795 polymorphisms with squamous cell carcinoma risk: A meta-analysis
  49. Exosomal HBV-DNA for diagnosis and treatment monitoring of chronic hepatitis B
  50. Shear stress leads to the dysfunction of endothelial cells through the Cav-1-mediated KLF2/eNOS/ERK signaling pathway under physiological conditions
  51. Interaction between the PI3K/AKT pathway and mitochondrial autophagy in macrophages and the leukocyte count in rats with LPS-induced pulmonary infection
  52. Meta-analysis of the rs231775 locus polymorphism in the CTLA-4 gene and the susceptibility to Graves’ disease in children
  53. Cloning, subcellular localization and expression of phosphate transporter gene HvPT6 of hulless barley
  54. Coptisine mitigates diabetic nephropathy via repressing the NRLP3 inflammasome
  55. Significant elevated CXCL14 and decreased IL-39 levels in patients with tuberculosis
  56. Whole-exome sequencing applications in prenatal diagnosis of fetal bowel dilatation
  57. Gemella morbillorum infective endocarditis: A case report and literature review
  58. An unusual ectopic thymoma clonal evolution analysis: A case report
  59. Severe cumulative skin toxicity during toripalimab combined with vemurafenib following toripalimab alone
  60. Detection of V. vulnificus septic shock with ARDS using mNGS
  61. Novel rare genetic variants of familial and sporadic pulmonary atresia identified by whole-exome sequencing
  62. The influence and mechanistic action of sperm DNA fragmentation index on the outcomes of assisted reproduction technology
  63. Novel compound heterozygous mutations in TELO2 in an infant with You-Hoover-Fong syndrome: A case report and literature review
  64. ctDNA as a prognostic biomarker in resectable CLM: Systematic review and meta-analysis
  65. Diagnosis of primary amoebic meningoencephalitis by metagenomic next-generation sequencing: A case report
  66. Phylogenetic analysis of promoter regions of human Dolichol kinase (DOLK) and orthologous genes using bioinformatics tools
  67. Collagen changes in rabbit conjunctiva after conjunctival crosslinking
  68. Effects of NM23 transfection of human gastric carcinoma cells in mice
  69. Oral nifedipine and phytosterol, intravenous nicardipine, and oral nifedipine only: Three-arm, retrospective, cohort study for management of severe preeclampsia
  70. Case report of hepatic retiform hemangioendothelioma: A rare tumor treated with ultrasound-guided microwave ablation
  71. Curcumin induces apoptosis in human hepatocellular carcinoma cells by decreasing the expression of STAT3/VEGF/HIF-1α signaling
  72. Rare presentation of double-clonal Waldenström macroglobulinemia with pulmonary embolism: A case report
  73. Giant duplication of the transverse colon in an adult: A case report and literature review
  74. Ectopic thyroid tissue in the breast: A case report
  75. SDR16C5 promotes proliferation and migration and inhibits apoptosis in pancreatic cancer
  76. Vaginal metastasis from breast cancer: A case report
  77. Screening of the best time window for MSC transplantation to treat acute myocardial infarction with SDF-1α antibody-loaded targeted ultrasonic microbubbles: An in vivo study in miniswine
  78. Inhibition of TAZ impairs the migration ability of melanoma cells
  79. Molecular complexity analysis of the diagnosis of Gitelman syndrome in China
  80. Effects of maternal calcium and protein intake on the development and bone metabolism of offspring mice
  81. Identification of winter wheat pests and diseases based on improved convolutional neural network
  82. Ultra-multiplex PCR technique to guide treatment of Aspergillus-infected aortic valve prostheses
  83. Virtual high-throughput screening: Potential inhibitors targeting aminopeptidase N (CD13) and PIKfyve for SARS-CoV-2
  84. Immune checkpoint inhibitors in cancer patients with COVID-19
  85. Utility of methylene blue mixed with autologous blood in preoperative localization of pulmonary nodules and masses
  86. Integrated analysis of the microbiome and transcriptome in stomach adenocarcinoma
  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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