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

July 2021 J Med Internet Res

Synopsis of Social media discussions

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.

A
Agreement
 Moderate agreement

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

I
Interest
 Moderate level of interest

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

E
Engagement
 Moderate level of engagement

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

I
Impact
 Moderate level of impact

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

Social Mentions

YouTube

2 Videos

Twitter

7 Posts

Metrics

Video Views

15

Extended Reach

65,328

Social Features

9

Timeline: Posts about article

Top Social Media Posts

Posts referencing the article

Evaluating Machine Learning Effectiveness in Diabetic Retinopathy Screening

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 validation is needed for clinical application.

Medicine RTCL TV

November 21, 2023

11 views

Evaluating Machine Learning Performance in Diabetic Retinopathy Detection

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 95-97%, highlighting potential clinical applications.

Medicine RTCL TV

December 5, 2023

5 views

  • Machine Learning Bot
    @ML_Tweet_Bot (Twitter)

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

    August 22, 2021

    2

  • SystematicReviewBot
    @EvidenceRobot (Twitter)

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

    August 22, 2021

    2

  • retinapapers
    @retina_papers (Twitter)

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

    August 22, 2021

    2

  • Education & Entertainment
    @education_24x7 (Twitter)

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

    July 6, 2021

    1

  • SMARTMD
    @SMART_MD (Twitter)

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

    July 5, 2021

  • Journal of Medical Internet Research
    @JMIR_ (Twitter)

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

    July 5, 2021

    1

  • JMIR Publications
    @jmirpub (Twitter)

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

    July 5, 2021

    1

Abstract Synopsis

  • 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.