Utilization of smartphone and tablet camera photographs to predict healing of diabetes-related foot ulcers
Open accessAbstract
The objective of this study was to build a machine learning model that can predict healing of diabetes-related foot ulcers, using both clinical attributes extracted from electronic health records (EHR) and image features extracted from photographs. The clinical information and photographs were collected at an academic podiatry wound clinic over a three-year period. Both hand-crafted color and texture features and deep learning-based features from the global average pooling layer of ResNet-50 were extracted from the wound photographs. Random Forest (RF) and Support Vector Machine (SVM) models were then trained for prediction. For prediction of eventual wound healing, the models built with hand-crafted imaging features alone outperformed models built with clinical or deep-learning features alone. Models trained with all features performed comparatively against models trained with hand-crafted imaging features. Utilization of smartphone and tablet photographs taken outside of research settings hold promise for predicting prognosis of diabetes-related foot ulcers.
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Kim et al. reported that ANN, RF, and SVM using smartphone images to estimate likelihood of healing of diabetes-related foot ulcers achieved AUC of 0.734, accuracy of 0.811, precision of 0.828, recall of 0.923, and F1 score of 0.873.
“the model could achieve AUC of 0.734, accuracy of 0.811, precision of 0.828, recall of 0.923, and F1 score of 0.873”
From Artificial intelligence in diabetes management: Advancements, opportunities, and challenges (2023) · cites this paper as Kim et al.115
The Discussion & conclusions reports an AUROC of 0.734 for models using all features. The Results says Table 2 reports accuracy, precision, recall, and F1 scores, but the passages in our copy do not establish the four quoted values. The Prediction models section identifies RF and SVM, not ANN, as the models built for this study. Page numbers are unavailable in our copy. (LLM, conf=0.86)
Experimental — accuracy not yet measuredOne study used smartphone images to estimate the likelihood of healing of diabetes-related foot ulcers with an AUC of 0.73.
“and the other study used smartphone images to estimate the likelihood of healing of diabetes-related foot ulcers (AUC = 0.73).115”
From Artificial intelligence in diabetes management: Advancements, opportunities, and challenges (2023) · cites this paper as [115]
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Artificial intelligence in diabetes management: Advancements, opportunities, and challenges
Guan, Z., Li, H., Liu, R., Cai, C., Liu, Y., Li, J., Wang, X., Huang, S., Wu, L., Liu, D., et al. (2023) · Cell Reports Medicine · doi:10.1016/j.xcrm.2023.101213 · Cited by 287 · see citing works
“115 For neuropathy screening, previous studies explored the development of AI systems to identify diabetic neuropathy based on EHRs 116 or corneal confocal microscopy images, 117–119 with AUCs ranging from 0.83 to 0.95.”
BackgroundInfluential
- Authors
- Kim, R. B., Gryak, J., Mishra, A., Cui, C., Soroushmehr, S. M. R., Najarian, K., & Wrobel, J. S.
- Affiliations
- University of Michigan (US)
- Corresponding author
- Kim, Renaid B.
- Venue
- Computers in Biology and Medicine
- Publisher
- Elsevier BV
- Published
- 2020-11
- Volume
- 126
- Pages
- 104042
- ISSN
- 0010-4825 (ISSN-L)
- PMC
- PMC9058995
- OpenAlex
- W3092615100
- Cited by
- 47
- References
- 4
- Citations by year
- 2026: 8 · 2025: 8 · 2024: 11 · 2023: 10 · 2022: 6 · 2021: 4
- FWCI
- 3.77
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Computers in Biology and Medicine (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Funders
- National Institutes of Health (17AU3752, DK076169, NIDDK U01)
- Topics
- Health Sciences › Medicine › Endocrinology, Diabetes and Metabolism › Diabetic Foot Ulcer Assessment and Management
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Radiomics and Machine Learning in Medical Imaging
- Keywords
- diabetic foot ulcer, wound healing prediction, wound imaging, color and texture features, electronic health records, clinical features, random forest, support vector machine, prognosis
- MeSH terms
- Smartphone, Machine Learning, Diabetes Mellitus, Humans, Wound Healing, Diabetic Foot, Support Vector Machine
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