Region-wise severity analysis of diabetic plantar foot thermograms
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Naveen Sharma
,
Satbir Singh
Abstract
Objectives
Diabetic foot ulcers (DFU) can be avoided if symptoms of diabetic foot complications are detected early and treated promptly. Early detection requires regular examination, which might be limited for many reasons. To identify affected or potentially affected regions in the diabetic plantar foot, the region-wise severity of the plantar foot must be known.
Methods
A novel thermal diabetic foot dataset of 104 subjects was developed that is suitable for Indian healthcare conditions. The entire plantar foot thermogram is divided into three parts, i.e., forefoot, midfoot, and hindfoot. The division of plantar foot is based on the prevalence of foot ulcers and the load on the foot. To classify the severity levels, conventional machine learning (CML) techniques like logistic regression, decision tree, KNN, SVM, random forest, etc., and convolutional neural networks (CNN), such as EfficientNetB1, VGG-16, VGG-19, AlexNet, InceptionV3, etc., were applied and compared for robust outcomes.
Results
The study successfully developed a thermal diabetic foot dataset, allowing for effective classification of diabetic foot ulcer severity using the CML and CNN techniques. The comparison of different methods revealed variations in performance, with certain approaches outperforming others.
Conclusions
The region-based severity analysis offers valuable insights for targeted interventions and preventive measures, contributing to a comprehensive assessment of diabetic foot ulcer severity. Further research and development in these techniques can enhance the detection and management of diabetic foot complications, ultimately improving patient outcomes.
Funding source: CSIR-Central Scientific Instruments Organization, Chandigarh (India) and Postgraduate Institute of Medical Education and Research (PGIMER), Chandigarh (India)
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Research funding: The authors acknowledge CSIR-Central Scientific Instruments Organization, Chandigarh (India) and Postgraduate Institute of Medical Education and Research (PGIMER), Chandigarh (India) for their support and encouragement in carrying out this research work.
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Author contributions: The contribution of each other are surmised as: Naveen Sharma (NS): Study conception and design, analysis and interpretation of results, manuscript writing/compilation; Sarfaraj Mirza (SM): Patient data collection, data design, and results, draft manuscript writing; Ashu Rastogi (AR): Medical inputs in designing protocol, review and manuscript editing; Satbir Singh(SS): Patient data collection, draft manuscript preparation, results compilation; Prasant Kumar Mahapatra (PKM): Critical review and editing.
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Competing interests: The authors have no competing of interest to disclose.
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Informed consent: Informed consent was obtained from all individuals included in this study.
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Ethical approval: The study was conducted at diabetic foot clinic of tertiary care health facility in North India. The protocol was approved (Ref. No-2020/000170) by the Research Ethics Committee of the PGIMER Chandigarh.
References
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© 2023 Walter de Gruyter GmbH, Berlin/Boston
Artikel in diesem Heft
- Frontmatter
- Review
- Biomechanical testing of osteosynthetic locking plates for proximal humeral shaft fractures – a systematic literature review
- Research Articles
- Instrumented treadmill for run biomechanics analysis: a comparative study
- Computational modelling of the graft-tunnel interaction in single-bundle ACL reconstructed knee
- Biomechanical effects of inclined implant shoulder design in all-on-four treatment concept: a three-dimensional finite element analysis
- Extension of the working time of dental composites due to a new type of white operating lamp
- Modeling the compliance of the human eye with elastic membranes based on a bionic approach
- Region-wise severity analysis of diabetic plantar foot thermograms
- A new method for vital sign detection using FMCW radar based on random body motion cancellation
- Atherosclerosis plaque tissue classification using self-attention-based conditional variational auto-encoder generative adversarial network using OCT plaque image
- A combined impedance compensation strategy applied to external automatic defibrillators
Artikel in diesem Heft
- Frontmatter
- Review
- Biomechanical testing of osteosynthetic locking plates for proximal humeral shaft fractures – a systematic literature review
- Research Articles
- Instrumented treadmill for run biomechanics analysis: a comparative study
- Computational modelling of the graft-tunnel interaction in single-bundle ACL reconstructed knee
- Biomechanical effects of inclined implant shoulder design in all-on-four treatment concept: a three-dimensional finite element analysis
- Extension of the working time of dental composites due to a new type of white operating lamp
- Modeling the compliance of the human eye with elastic membranes based on a bionic approach
- Region-wise severity analysis of diabetic plantar foot thermograms
- A new method for vital sign detection using FMCW radar based on random body motion cancellation
- Atherosclerosis plaque tissue classification using self-attention-based conditional variational auto-encoder generative adversarial network using OCT plaque image
- A combined impedance compensation strategy applied to external automatic defibrillators
