With the development of artificial intelligence, machine learning has been widely used in diagnosis of diseases. It is crucial to conduct diagnostic test accuracy studies and evaluate the performance of models reasonably to improve the accuracy of diagnosis. For machine learning-based diagnostic test accuracy studies, this paper introduces the principles of study design in the aspects of target conditions, selection of participants, diagnostic tests, reference standards and ethics.
ObjectiveTo systematically review the value of deep learning (DL) on the diagnosis of diabetic retinopathy (DR) based on color fundus photographs. MethodsThe PubMed, Embase, Web of Science, Cochrane Library, IEEE, CNKI, VIP, WanFang Data databases were systematically searched to collect the studies on the use of DL in the diagnosis of DR from January 2019 to November 2024. Two reviewers independently screened literature, extracted data and assessed the risk of bias of the included studies. Meta-analysis was then performed by using RevMan 5.4.1, Meta-Disc 1.4 and Stata 16.0 software. ResultsA total of 16 literatures were included, involving 215 560 images. Meta-analysis results showed that the combined sensitivity of DL in diagnosing DR was 0.97 (95%CI 0.94 to 0.98), the specificity was 0.97 (95%CI 0.94 to 0.98), the AUC was 0.99 (95%CI 0.94 to 0.98), and the DOR was 852 (95%CI 403 to 1 803). ConclusionDL has a high diagnostic value for DR. However, there is a high degree of heterogeneity among different studies. In the future, more large-sample, high-quality studies can be included to confirm its clinical applicability.
ObjectiveTo systematically review the diagnostic value of optical coherence tomography angiography (OCTA) for primary open-angle glaucoma (POAG). MethodsThe CNKI, WanFang Data, VIP, CBM, PubMed, Embase, Web of Science, and Cochrane Library databases were electronically searched to collect diagnostic test on OCTA for POAG from inception to February 2024. Two reviewers independently screened literature, extracted data and assessed the risk of bias of the included studies. Meta-analysis was then performed by using Stata 15.0 software. ResultsA total of 12 diagnostic tests involving 993 subjects were included. Meta-analysis results showed that the sensitivity/specificity of OCTA for diagnosing peripapillary vessel density, retinal vessel density, and optic nerve fiber changes in patients with POAG were 0.77/0.92, 0.56/0.92, and 0.85/0.91, respectively, and the AUC of the SROC curve was 0.94, 0.92 and 0.95, respectively. ConclusionOCTA has high diagnostic accuracy for POAG. Due to the limited quality and quantity of the included studies, more high quality studies are needed to verify the above conclusion.
Machine learning-based diagnostic tests have certain differences of measurement indicators with traditional diagnostic tests. In this paper, we elaborate the definitions, calculation methods and statistical inferences of common measurement indicators of machine learning-based diagnosis models in detail. We hope that this paper will be helpful for clinical researchers to better evaluate machine learning diagnostic models.
