Deep learning versus ophthalmologists for screening for glaucoma on fundus examination: A systematic review and meta-analysis

Mathieu Buisson, Valentin Navel*, Antoine Labbé, Stephanie L. Watson, Julien S. Baker, Patrick Murtagh, Frédéric Chiambaretta, Frédéric Dutheil

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)

Abstract

Background

In this systematic review and meta-analysis, we aimed to compare deep learning versus ophthalmologists in glaucoma diagnosis on fundus examinations. 

Method

PubMed, Cochrane, Embase, ClinicalTrials.gov and ScienceDirect databases were searched for studies reporting a comparison between the glaucoma diagnosis performance of deep learning and ophthalmologists on fundus examinations on the same datasets, until 10 December 2020. Studies had to report an area under the receiver operating characteristics (AUC) with SD or enough data to generate one. 

Results

We included six studies in our meta-analysis. There was no difference in AUC between ophthalmologists (AUC = 82.0, 95% confidence intervals [CI] 65.4–98.6) and deep learning (97.0, 89.4–104.5). There was also no difference using several pessimistic and optimistic variants of our meta-analysis: the best (82.2, 60.0–104.3) or worst (77.7, 53.1–102.3) ophthalmologists versus the best (97.1, 89.5–104.7) or worst (97.1, 88.5–105.6) deep learning of each study. We did not retrieve any factors influencing those results. 

Conclusion

Deep learning had similar performance compared to ophthalmologists in glaucoma diagnosis from fundus examinations. Further studies should evaluate deep learning in clinical situations.

Original languageEnglish
Pages (from-to)1027-1038
Number of pages12
JournalClinical and Experimental Ophthalmology
Volume49
Issue number9
Early online date10 Sep 2021
DOIs
Publication statusPublished - Dec 2021

Scopus Subject Areas

  • Ophthalmology

User-Defined Keywords

  • artificial intelligence
  • deep learning
  • glaucoma
  • machine learning
  • screening

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