Towards ‘automated gonioscopy’: a deep learning algorithm for 360° angle assessment by swept-source optical coherence tomography

• Published in: British journal of ophthalmology Vol. 106; no. 10; pp. 1387 - 1392
• Main Authors: Porporato, Natalia, Tun, Tin A, Baskaran, Mani, Wong, Damon W K, Husain, Rahat, Fu, Huazhu, Sultana, Rehena, Perera, Shamira, Schmetterer, Leopold, Aung, Tin
• Format: Journal Article
• Language: English
• Published: BMA House, Tavistock Square, London, WC1H 9JR BMJ Publishing Group Ltd 01.10.2022; BMJ Publishing Group LTD
• Subjects: Algorithms; angle; Automation; Classification; Clinical science; Deep learning; Glaucoma; imaging; Neural networks; Software; Tomography; United States > US; Singapore; California; angle; imaging; glaucoma
• ISSN: 0007-1161; 1468-2079; 1468-2079
• DOI: 10.1136/bjophthalmol-2020-318275

AimsTo 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).MethodsThis 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).ResultsApproximately 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.ConclusionsThe 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.