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Towards ‘automated gonioscopy’: a deep learning algorithm for 360° angle assessment by swept-source optical coherence tomography
  1. Natalia Porporato1,
  2. Tin A Tun1,
  3. Mani Baskaran1,
  4. Damon W K Wong1,2,
  5. Rahat Husain1,
  6. Huazhu Fu3,
  7. Rehena Sultana4,
  8. Shamira Perera1,4,
  9. Leopold Schmetterer1,2,5,6,7,
  10. Tin Aung1,8
  1. 1Singapore Eye Research Institute/Singapore National Eye Centre, Singapore
  2. 2SERI-NTU Advanced Ocular Engineering (STANCE) Program, Nanyang Technological University, Singapore
  3. 3Inception Institute of Artificial Intelligence, Abu Dhabi, UAE
  4. 4Duke-NUS Graduate Medical School, Singapore
  5. 5School of Chemical and Biomedical Engineering, Nanyang Technological University, Singapore
  6. 6Department of Clinical Pharmacology, Medical University of Vienna, Austria, Austria
  7. 7Center for Medical Physics and Biomedical Engineering, Medical University of Vienna, Austria, Austria
  8. 8Yong Loo Lin School of Medicine, National University of Singapore, Singapore
  1. Correspondence to Dr Tin Aung, Glaucoma, Singapore Eye Research Institute, Singapore 168751, Singapore; aung.tin{at}snec.com.sg

Abstract

Aims To validate a deep learning (DL) algorithm (DLA) for 360° angle assessment on swept-source optical coherence tomography (SS-OCT) (CASIA SS-1000, Tomey Corporation, Nagoya, Japan).

Methods This was a reliability analysis from a cross-sectional study. An independent test set of 39 936 SS-OCT scans from 312 phakic subjects (128 SS-OCT meridional scans per eye) was analysed. Participants above 50 years with no previous history of intraocular surgery were consecutively recruited from glaucoma clinics. Indentation gonioscopy and dark room SS-OCT were performed. Gonioscopic angle closure was defined as non-visibility of the posterior trabecular meshwork in ≥180° of the angle. For each subject, all images were analysed by a DL-based network based on the VGG-16 architecture, for gonioscopic angle-closure detection. Area under receiver operating characteristic curves (AUCs) and other diagnostic performance indicators were calculated for the DLA (index test) against gonioscopy (reference standard).

Results Approximately 80% of the participants were Chinese, and more than half were women (57.4%). The prevalence of gonioscopic angle closure in this hospital-based sample was 20.2%. After analysing a total of 39 936 SS-OCT scans, the AUC of the DLA was 0.85 (95% CI:0.80 to 0.90, with sensitivity of 83% and a specificity of 87%) to classify gonioscopic angle closure with the optimal cut-off value of >35% of circumferential angle closure.

Conclusions The DLA exhibited good diagnostic performance for detection of gonioscopic angle closure on 360° SS-OCT scans in a glaucoma clinic setting. Such an algorithm, independent of the identification of the scleral spur, may be the foundation for a non-contact, fast and reproducible ‘automated gonioscopy’ in future.

  • angle
  • glaucoma
  • imaging

Data availability statement

Data are available upon reasonable request. Deidentified participant data available upon request to Singapore Eye Research Institute (https://orcid.org/0000-0002-7916-0589).

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Data availability statement

Data are available upon reasonable request. Deidentified participant data available upon request to Singapore Eye Research Institute (https://orcid.org/0000-0002-7916-0589).

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Footnotes

  • Contributors Conception or design of the work: TA and RH. Acquisition: TAT, RH, SP and TA. Analysis or interpretation of data: NP, DWKW, HF, RS, SP, LP and TA.

  • Funding This work was supported by grants from National Medical Research Council and Biomedical Research Council, Singapore (Grant No. 10/1/35/19/674).

  • Competing interests None declared.

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

  • Supplemental material This content has been supplied by the author(s). It has not been vetted by BMJ Publishing Group Limited (BMJ) and may not have been peer-reviewed. Any opinions or recommendations discussed are solely those of the author(s) and are not endorsed by BMJ. BMJ disclaims all liability and responsibility arising from any reliance placed on the content. Where the content includes any translated material, BMJ does not warrant the accuracy and reliability of the translations (including but not limited to local regulations, clinical guidelines, terminology, drug names and drug dosages), and is not responsible for any error and/or omissions arising from translation and adaptation or otherwise.

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