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Generalisability and performance of an OCT-based deep learning classifier for community-based and hospital-based detection of gonioscopic angle closure
  1. Jasmeen Randhawa1,
  2. Michael Chiang2,
  3. Natalia Porporato3,
  4. Anmol A Pardeshi2,
  5. Justin Dredge2,
  6. Galo Apolo Aroca2,
  7. Tin A Tun3,
  8. Joanne HuiMin Quah4,
  9. Marcus Tan5,
  10. Risa Higashita6,
  11. Tin Aung3,5,
  12. Rohit Varma7,
  13. Benjamin Y Xu1
  1. 1Keck School of Medicine of the University of Southern California, Los Angeles, California, USA
  2. 2Roski Eye Institute, Keck School of Medicine of the University of Southern California, Los Angeles, California, USA
  3. 3Singapore National Eye Centre, Singapore Eye Research Institute, Singapore
  4. 4Outram Polyclinic, SingHealth Polyclinics, Singapore
  5. 5Ophthalmology, National University of Singapore, Singapore
  6. 6Tomey Corporation, Nagoya, Japan
  7. 7Southern California Eye Institute, CHA Hollywood Presbyterian Medical Center, Los Angeles, California, USA
  1. Correspondence to Dr Benjamin Y Xu, Ophthalmology, University of Southern California Keck School of Medicine, Los Angeles, CA 90033, USA; Benjamin.Xu{at}


Purpose To assess the generalisability and performance of a deep learning classifier for automated detection of gonioscopic angle closure in anterior segment optical coherence tomography (AS-OCT) images.

Methods A convolutional neural network (CNN) model developed using data from the Chinese American Eye Study (CHES) was used to detect gonioscopic angle closure in AS-OCT images with reference gonioscopy grades provided by trained ophthalmologists. Independent test data were derived from the population-based CHES, a community-based clinic in Singapore, and a hospital-based clinic at the University of Southern California (USC). Classifier performance was evaluated with receiver operating characteristic curve and area under the receiver operating characteristic curve (AUC) metrics. Interexaminer agreement between the classifier and two human examiners at USC was calculated using Cohen’s kappa coefficients.

Results The classifier was tested using 640 images (311 open and 329 closed) from 127 Chinese Americans, 10 165 images (9595 open and 570 closed) from 1318 predominantly Chinese Singaporeans and 300 images (234 open and 66 closed) from 40 multiethnic USC patients. The classifier achieved similar performance in the CHES (AUC=0.917), Singapore (AUC=0.894) and USC (AUC=0.922) cohorts. Standardising the distribution of gonioscopy grades across cohorts produced similar AUC metrics (range 0.890–0.932). The agreement between the CNN classifier and two human examiners (Ҡ=0.700 and 0.704) approximated interexaminer agreement (Ҡ=0.693) in the USC cohort.

Conclusion An OCT-based deep learning classifier demonstrated consistent performance detecting gonioscopic angle closure across three independent patient populations. This automated method could aid ophthalmologists in the assessment of angle status in diverse patient populations.

  • glaucoma
  • anterior chamber
  • diagnostic tests/investigation
  • imaging

Data availability statement

All data relevant to the study are included in the article or uploaded as supplementary information.

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Data availability statement

All data relevant to the study are included in the article or uploaded as supplementary information.

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  • Contributors BYX and TA conceived of the presented study. AAP, JD, JHQ, MT, TA, RV and BYX collected the data. JR, MC, NP, GAA, RH and BYX analysed the data. BYX is the guarantor of the study. All authors discussed the results and contributed to the final manuscript.

  • Funding This work was supported by grants U10 EY017337 and K23 EY029763 from the National Eye Institute, National Institute of Health, Bethesda, Maryland; a Young Clinician Scientist Research Award from the American Glaucoma Society, San Francisco, California; a Grant-in-Aid Research Award from Fight for Sight, New York, New York; an SC-CTSI Clinical and Community Research Award from the Southern California Clinical and Translational Science Institute, Los Angeles, California; and an unrestricted grant to the Department of Ophthalmology from Research to Prevent Blindness, New York, New York.

  • Competing interests None declared.

  • Provenance and peer review Not commissioned; externally peer reviewed.

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