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.
扫码关注我们
求助内容:
应助结果提醒方式:
