Diagnostic value of artificial intelligence assisted diagnostic system for acute leukemia: a Meta-analysis

doi:10.3969/j.issn.1006-7795.2024.04.020

Abstract

Abstract: Objective Using Meta-analysis to comprehensively evaluate the potential of artificial intelligence(AI) in assisting the diagnosis of acute leukemia. Methods We delved into databases including Ovid-Medline, Embase, IEEE and Cochrane Library, meticulously hunting for trials that hamessed AI for diagnosing acute leukemia .Our search spanned from inception until May 1st, 2023. After sifting through literature and data extraction by two independent reviewers, and subsequent evaluation of the potential bias in the selected studies, we conducted Meta-analysis using Stata 17.0, RevMan 5.4 and Meta-Disc 1.4 software to obtain the results. Results Analysis a total of 15 studies, involving a whopping 20 214 images. The Meta-analysis results that the sensitivity, specificity, positive likelihood ratio (PLR), negative likelihood ratio(NLR), diagnostic odds ratio (DOR), for AI-assisted acute leukemia screening stood at 0.96 （95% CI: 0.92-0.97), 0.97 （95% CI: 0.94-0.98), 29.9 （95% CI: 17.2-51.9), 0.05 （95% CI: 0.03-0.08), 652 （95% CI: 290-1 464), and a staggering 97%, respectively. The area under the curve (AUC) on the summary receiver operating characteristic (SROC) graph clocked in at 0.99 （95% CI: 0.98-1.0). Conclusions AI technology has high sensitivity, specificity and higher AUC value in screening and early diagnosis of acute leukemia，It has potential clinical application value, however, Due to limitations in the quantity and quality of included studies causing significant heterogeneity between studies, further analysis is needed on the potential sources of this heterogeneity, provide more accurate and reliable basis for the standardization of AI assisted diagnosis in acute leukemia. This study has been registered with PROSPERO registration number CRD42023480455.

Key words: artifical intelligence, , acute leukemia, , diagnosis, , screening

CLC Number:

R557
TP183

Zhang Daqian, Zhang Xiaoxin, Ye Zichen, Xie Zhilan, Yang Jichun, Jiang Yu. Diagnostic value of artificial intelligence assisted diagnostic system for acute leukemia: a Meta-analysis[J]. Journal of Capital Medical University, 2024, 45(4): 693-700.

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URL: https://journal03.magtech.org.cn/Jweb_sdykdxxb/EN/10.3969/j.issn.1006-7795.2024.04.020

https://journal03.magtech.org.cn/Jweb_sdykdxxb/EN/Y2024/V45/I4/693

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