J Med Internet Res July 2021 PMID 34407500

Performance and Limitation of Machine Learning Algorithms for Diabetic Retinopathy Screening: Meta-analysis.

Jo-Hsuan Wu, T Y Alvin Liu, Wan-Ting Hsu, Jennifer Hui-Chun Ho, Chien-Chang Lee

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Peak: 4 in Jul 2021

across 2 sources since Jul 2021

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Jul 2021 – Dec 2023

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What people are saying

Discussions highlight the article's comprehensive meta-analysis of ML algorithms, with some posts sharing the publication links to emphasize its relevance, and using terms like 'new in JMIR' and 'performance and limitations' to underscore the study’s importance in evaluating current AI methods for diabetic retinopathy detection.

AgreementModerate agreement

Most discussions acknowledge the study’s thorough analysis and generally agree with its findings about ML performance in DR screening.

InterestModerate interest

Participants show moderate interest by engaging with the publication and sharing links, indicating curiosity but not intense focus.

EngagementModerate engagement

Comments suggest a willingness to explore the article more deeply, as they share sources and frame the publication as noteworthy.

ImpactModerate impact

The tone reflects recognition of the study’s significance within medical AI research, pointing toward its influence on future clinical practices.

Abstract in brief

This study systematically reviews the accuracy of machine learning algorithms in diagnosing diabetic retinopathy (DR) from color fundus photographs, finding that ML methods generally achieve high diagnostic performance with AUROC values close to 1.0, indicating excellent accuracy.
The research analyzed data from 60 studies involving over 445,000 interpretations, revealing that ML tools are effective across different DR categories, including more-than-mild DR, with sensitivity around 95-97%.
Despite promising results, the study highlights the need for further discussion on the best ML techniques for real-world clinical application and emphasizes that current ML-based screening shows high potential but still requires validation for widespread use.

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All mentions

Evaluating Machine Learning Effectiveness in Diabetic Retinopathy Screening

This study reviews the accuracy of machine learning algorithms diagnosing diabetic retinopathy from fundus photographs, showing high diagnostic performance with AUROC near 1.0 and sensitivities around 95-97%. Further…

Medicine RTCL TV · Nov 21, 2023 11 views

Evaluating Machine Learning Performance in Diabetic Retinopathy Detection

This study systematically reviews the accuracy of machine learning algorithms diagnosing diabetic retinopathy from fundus photographs, showing high diagnostic performance with AUROC near 1.0 and sensitivities around…

Medicine RTCL TV · Dec 5, 2023 5 views

Machine Learning Bot

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RT @retina_papers: Performance and Limitation of Machine Learning Algorithms for Diabetic Retinopathy Screening: Meta-analysis https://t.co…

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retinapapers

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Performance and Limitation of Machine Learning Algorithms for Diabetic Retinopathy Screening: Meta-analysis https://t.co/cd4xIK1tJx https://t.co/yiAUqzloE3

Aug 22, 2021 · 2 shares · 145 followers View post →

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RT @AINewsFeed: Journal of Medical Internet Research - Performance and Limitation of Machine Learning Algorithms f... https://t.co/L4Qym1da…

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SMARTMD

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Performance and Limitation of Machine Learning Algorithms for Diabetic Retinopathy Screening: Meta-analysis via @jmirpub https://t.co/sbbB2mhqPl

Jul 5, 2021 · 3,869 followers View post →

Journal of Medical Internet Research

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RT @jmirpub: New in JMIR: Performance and Limitation of Machine Learning Algorithms for Diabetic Retinopathy Screening: Meta-analysis https…

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JMIR Publications

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New in JMIR: Performance and Limitation of Machine Learning Algorithms for Diabetic Retinopathy Screening: Meta-analysis https://t.co/6D4uMFXmS6 https://t.co/2dCsfzUdoS

Jul 5, 2021 · 1 share · 35,896 followers View post →