Diabetes research and clinical practice · 2026
Accuracy of deep learning for risk prediction and screening of diabetic foot Ulcers: A systematic review and meta-analysis.
СтатьяСистематический обзорМетаанализНазвание и аннотация: Перевод пока не подготовлен
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Zhao Tongling, Yu Jing, Deng Ruixue, Ding Lu, Li Xiangyan, Mi Jia
Аннотация
BACKGROUND: This research sought to systematically estimate the accuracy of deep learning (DL) in diagnosing diabetic foot ulcers (DFUs), thereby providing novel insights for the development and updating of artificial intelligence (AI)-assisted tools.
METHODS: This study searched PubMed, Cochrane, Embase, and Web of Science up to October 2025 for original studies that used image-based DL to detect DFUs. The risk of bias of the included studies was estimated utilizing QUADAS-AI. In the meta-analysis, we constructed diagnostic 2 × 2 tables and applied a bivariate mixed-effects model.
RESULTS: In total, 55 studies were included. Of these, 32 were included in the meta-analysis, involving 87 diagnostic 2 × 2 tables for validating DL models. The pooled results exhibited that for the overall validation sets, the sensitivity and specificity were 0.96 (95% CI: 0.94-0.98) and 0.97 (95% CI: 0.94-0.98), respectively. In multicenter datasets, the sensitivity, specificity, 0.87 (95% CI: 0.68-0.96), 0.92 (95% CI: 0.82-0.97).
CONCLUSIONS: This research discloses that image-based DL for detecting DFUs might be a promising approach. Future studies should further ascertain the generalizability of models derived from public databases to individual-level data across diverse geographic regions, thereby providing evidence for the evaluation and development of AI-assisted tools.
METHODS: This study searched PubMed, Cochrane, Embase, and Web of Science up to October 2025 for original studies that used image-based DL to detect DFUs. The risk of bias of the included studies was estimated utilizing QUADAS-AI. In the meta-analysis, we constructed diagnostic 2 × 2 tables and applied a bivariate mixed-effects model.
RESULTS: In total, 55 studies were included. Of these, 32 were included in the meta-analysis, involving 87 diagnostic 2 × 2 tables for validating DL models. The pooled results exhibited that for the overall validation sets, the sensitivity and specificity were 0.96 (95% CI: 0.94-0.98) and 0.97 (95% CI: 0.94-0.98), respectively. In multicenter datasets, the sensitivity, specificity, 0.87 (95% CI: 0.68-0.96), 0.92 (95% CI: 0.82-0.97).
CONCLUSIONS: This research discloses that image-based DL for detecting DFUs might be a promising approach. Future studies should further ascertain the generalizability of models derived from public databases to individual-level data across diverse geographic regions, thereby providing evidence for the evaluation and development of AI-assisted tools.