Journal of tissue viability · 2025
Diagnostic accuracy of artificial intelligence to identify diabetic foot ulcers: A systematic review and meta-analysis.
СтатьяСистематический обзорМетаанализНазвание и аннотация: Перевод пока не подготовлен
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Chen Zhigang, Liu Xinliang, Li Simeng, Wu Zhenheng, Tan Haifen, Yu Fuqian, Wang Dongmei, Zhu Changhua, Lin Pengfei
Аннотация
BACKGROUND: The accuracy of artificial intelligence (AI) in diagnosing diabetic foot ulcers (DFUs) in dermatology remains uncertain.
OBJECTIVE: To summarize the diagnostic accuracy of AI for DFUs and to provide specific theoretical basis for clinical diagnosis.
METHODS: From the inception of the database up to November 3, 2024, we performed an extensive search across several databases, including PubMed, Web of Science (WoS), Embase, Scopus, the Cochrane Library, Wanfang, and the China National Knowledge Infrastructure (CNKI). To assess the overall efficacy of AI in diagnostic testing, we utilized combined metrics of sensitivity, specificity, positive likelihood ratio (PLR), negative likelihood ratio (NLR), diagnostic odds ratio (DOR), and the area under the curve (AUC). Finally, we assessed the presence of publication bias using the Deeks' funnel plot asymmetry test.
RESULTS: In this meta-analysis, a total of 16 references were identified. The summary diagnostic performance is as follows: sensitivity, 0.89 (95 % CI, 0.85-0.92); specificity, 0.93 (95 % CI, 0.90-0.95); PLR, 6.31 (95 % CI, 5.67-7.02); NLR, 0.14 (95 % CI, 0.12-0.15); DOR, 58.22 (95 % CI, 50.18-67.55); and AUC, 0.97 (95 % CI, 0.95-0.98). Subgroup analysis showed the best performance observed in studies with 300 to 1000 samples. Furthermore, the Fagan plot indicates an increase in post-test probability from 10 % pre-test to 59 % post-test.
CONCLUSION: In summary, our results suggest that AI has high accuracy in diagnosing DFUs.
OBJECTIVE: To summarize the diagnostic accuracy of AI for DFUs and to provide specific theoretical basis for clinical diagnosis.
METHODS: From the inception of the database up to November 3, 2024, we performed an extensive search across several databases, including PubMed, Web of Science (WoS), Embase, Scopus, the Cochrane Library, Wanfang, and the China National Knowledge Infrastructure (CNKI). To assess the overall efficacy of AI in diagnostic testing, we utilized combined metrics of sensitivity, specificity, positive likelihood ratio (PLR), negative likelihood ratio (NLR), diagnostic odds ratio (DOR), and the area under the curve (AUC). Finally, we assessed the presence of publication bias using the Deeks' funnel plot asymmetry test.
RESULTS: In this meta-analysis, a total of 16 references were identified. The summary diagnostic performance is as follows: sensitivity, 0.89 (95 % CI, 0.85-0.92); specificity, 0.93 (95 % CI, 0.90-0.95); PLR, 6.31 (95 % CI, 5.67-7.02); NLR, 0.14 (95 % CI, 0.12-0.15); DOR, 58.22 (95 % CI, 50.18-67.55); and AUC, 0.97 (95 % CI, 0.95-0.98). Subgroup analysis showed the best performance observed in studies with 300 to 1000 samples. Furthermore, the Fagan plot indicates an increase in post-test probability from 10 % pre-test to 59 % post-test.
CONCLUSION: In summary, our results suggest that AI has high accuracy in diagnosing DFUs.