ArticleFrontiers in public health2022
Image segmentation using transfer learning and Fast R-CNN for diabetic foot wound treatments.
Article in Frontiers in public health, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers, 1 of them a synthesis that pooled it.
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Who cites it
12 citing papers in PubMed, 1 synthesis or guideline pooled it, 33 citations in OpenAlex.
- Artificial Intelligence Methods for Diagnostic and Decision-Making Assistance in Chronic Wounds: A Systematic Review.Journal of medical systems · 2025Pooled it
- Advances in the application of artificial intelligence-driven multi-modal imaging technologies in the comprehensive diagnosis and treatment of diabetic foot ulcers.Reviews in endocrine & metabolic disorders · 2026Review
- AI-Based Models for Diabetic Foot Ulcer Assessment: Scoping Review.JMIR diabetes · 2026Review
- Deep-learning-based diagnostic models for transcutaneous oxygen pressure-defined peripheral arterial disease and lower extremity wound healing in patients with diabetic foot.BMC medical informatics and decision making · 2026Article
- Precision nutrition in diabetic foot ulcers: multimodal artificial intelligence for personalized metabolic management.Frontiers in nutrition · 2026Review
- ClearSight-RS: A YOLOv5-Based Network with Dynamic Enhancement for Remote Sensing Small Target Detection.Sensors (Basel, Switzerland) · 2025Article
- Advances in Image-Based Diagnosis of Diabetic Foot Ulcers Using Deep Learning and Machine Learning: A Systematic Review.Biomedicines · 2025Review
- Knowledge, attitudes, and practices among patients with diabetic foot ulcers towards disease management.Scientific reports · 2025Article
- Advancing Diabetic Foot Ulcer Care: AI and Generative AI Approaches for Classification, Prediction, Segmentation, and Detection.Healthcare (Basel, Switzerland) · 2025Review
- Federated multimodal AI for precision-equitable diabetes care.Frontiers in digital health · 2025Review
- Construction and validation of a deep learning-based diagnostic model for segmentation and classification of diabetic foot.Frontiers in endocrinology · 2025Article
- The role of machine learning in advancing diabetic foot: a review.Frontiers in endocrinology · 2024Review
Corrections and comments
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Authors and funding
7 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Diabetic foot ulcers (DFUs) are considered the most challenging forms of chronic ulcerations to handle their multifactorial nature. It is necessary to establish a comprehensive treatment plan, accurate, and systematic evaluation of a patient with a DFU. This paper proposed an image recognition of diabetic foot wounds to support the effective execution of the treatment plan. In the severity of a diabetic foot ulcer, we refer to the current qualitative evaluation method commonly used in clinical practice, developed by the International Working Group on the Diabetic Foot: PEDIS index, and the evaluation made by physicians. The deep neural network, convolutional neural network, object recognition, and other technologies are applied to analyze the classification, location, and size of wounds by image analysis technology. The image features are labeled with the help of the physician. The Object Detection Fast R-CNN method is applied to these wound images to build and train machine learning modules and evaluate their effectiveness. In the assessment accuracy, it can be indicated that the wound image detection data can be as high as 90%.
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Registered trials
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