Performance of artificial intelligence in diabetic retinopathy screening: a systematic review and meta-analysis of prospective studies
AimsTo systematically evaluate the diagnostic value of an artificial intelligence (AI) algorithm model for various types of diabetic retinopathy (DR) in prospective studies over the previous five years, and to explore the factors affecting its diagnostic effectiveness.Materials and methodsA search w...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2023-06-01
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| Series: | Frontiers in Endocrinology |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fendo.2023.1197783/full |
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| author | Zhibin Wang Zhaojin Li Kunyue Li Siyuan Mu Xiaorui Zhou Yu Di |
| author_facet | Zhibin Wang Zhaojin Li Kunyue Li Siyuan Mu Xiaorui Zhou Yu Di |
| author_sort | Zhibin Wang |
| collection | DOAJ |
| description | AimsTo systematically evaluate the diagnostic value of an artificial intelligence (AI) algorithm model for various types of diabetic retinopathy (DR) in prospective studies over the previous five years, and to explore the factors affecting its diagnostic effectiveness.Materials and methodsA search was conducted in Cochrane Library, Embase, Web of Science, PubMed, and IEEE databases to collect prospective studies on AI models for the diagnosis of DR from January 2017 to December 2022. We used QUADAS-2 to evaluate the risk of bias in the included studies. Meta-analysis was performed using MetaDiSc and STATA 14.0 software to calculate the combined sensitivity, specificity, positive likelihood ratio, and negative likelihood ratio of various types of DR. Diagnostic odds ratios, summary receiver operating characteristic (SROC) plots, coupled forest plots, and subgroup analysis were performed according to the DR categories, patient source, region of study, and quality of literature, image, and algorithm.ResultsFinally, 21 studies were included. Meta-analysis showed that the pooled sensitivity, specificity, pooled positive likelihood ratio, pooled negative likelihood ratio, area under the curve, Cochrane Q index, and pooled diagnostic odds ratio of AI model for the diagnosis of DR were 0.880 (0.875-0.884), 0.912 (0.99-0.913), 13.021 (10.738-15.789), 0.083 (0.061-0.112), 0.9798, 0.9388, and 206.80 (124.82-342.63), respectively. The DR categories, patient source, region of study, sample size, quality of literature, image, and algorithm may affect the diagnostic efficiency of AI for DR.ConclusionAI model has a clear diagnostic value for DR, but it is influenced by many factors that deserve further study.Systematic review registrationhttps://www.crd.york.ac.uk/prospero/, identifier CRD42023389687. |
| first_indexed | 2024-03-13T05:56:43Z |
| format | Article |
| id | doaj.art-3be5b9926b5d4811bde562cb2900fffa |
| institution | Directory Open Access Journal |
| issn | 1664-2392 |
| language | English |
| last_indexed | 2024-03-13T05:56:43Z |
| publishDate | 2023-06-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Endocrinology |
| spelling | doaj.art-3be5b9926b5d4811bde562cb2900fffa2023-06-13T04:50:28ZengFrontiers Media S.A.Frontiers in Endocrinology1664-23922023-06-011410.3389/fendo.2023.11977831197783Performance of artificial intelligence in diabetic retinopathy screening: a systematic review and meta-analysis of prospective studiesZhibin WangZhaojin LiKunyue LiSiyuan MuXiaorui ZhouYu DiAimsTo systematically evaluate the diagnostic value of an artificial intelligence (AI) algorithm model for various types of diabetic retinopathy (DR) in prospective studies over the previous five years, and to explore the factors affecting its diagnostic effectiveness.Materials and methodsA search was conducted in Cochrane Library, Embase, Web of Science, PubMed, and IEEE databases to collect prospective studies on AI models for the diagnosis of DR from January 2017 to December 2022. We used QUADAS-2 to evaluate the risk of bias in the included studies. Meta-analysis was performed using MetaDiSc and STATA 14.0 software to calculate the combined sensitivity, specificity, positive likelihood ratio, and negative likelihood ratio of various types of DR. Diagnostic odds ratios, summary receiver operating characteristic (SROC) plots, coupled forest plots, and subgroup analysis were performed according to the DR categories, patient source, region of study, and quality of literature, image, and algorithm.ResultsFinally, 21 studies were included. Meta-analysis showed that the pooled sensitivity, specificity, pooled positive likelihood ratio, pooled negative likelihood ratio, area under the curve, Cochrane Q index, and pooled diagnostic odds ratio of AI model for the diagnosis of DR were 0.880 (0.875-0.884), 0.912 (0.99-0.913), 13.021 (10.738-15.789), 0.083 (0.061-0.112), 0.9798, 0.9388, and 206.80 (124.82-342.63), respectively. The DR categories, patient source, region of study, sample size, quality of literature, image, and algorithm may affect the diagnostic efficiency of AI for DR.ConclusionAI model has a clear diagnostic value for DR, but it is influenced by many factors that deserve further study.Systematic review registrationhttps://www.crd.york.ac.uk/prospero/, identifier CRD42023389687. https://www.frontiersin.org/articles/10.3389/fendo.2023.1197783/fullartificial intelligencediabetic retinopathymeta-analysisdiagnostic accuracyprospective study |
| spellingShingle | Zhibin Wang Zhaojin Li Kunyue Li Siyuan Mu Xiaorui Zhou Yu Di Performance of artificial intelligence in diabetic retinopathy screening: a systematic review and meta-analysis of prospective studies Frontiers in Endocrinology artificial intelligence diabetic retinopathy meta-analysis diagnostic accuracy prospective study |
| title | Performance of artificial intelligence in diabetic retinopathy screening: a systematic review and meta-analysis of prospective studies |
| title_full | Performance of artificial intelligence in diabetic retinopathy screening: a systematic review and meta-analysis of prospective studies |
| title_fullStr | Performance of artificial intelligence in diabetic retinopathy screening: a systematic review and meta-analysis of prospective studies |
| title_full_unstemmed | Performance of artificial intelligence in diabetic retinopathy screening: a systematic review and meta-analysis of prospective studies |
| title_short | Performance of artificial intelligence in diabetic retinopathy screening: a systematic review and meta-analysis of prospective studies |
| title_sort | performance of artificial intelligence in diabetic retinopathy screening a systematic review and meta analysis of prospective studies |
| topic | artificial intelligence diabetic retinopathy meta-analysis diagnostic accuracy prospective study |
| url | https://www.frontiersin.org/articles/10.3389/fendo.2023.1197783/full |
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