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Diagnostic accuracy of swept source optical coherence tomography classification algorithms for detection of gonioscopic angle closure
  1. Shayne S Tan1,2,3,
  2. Tin A Tun2,3,
  3. Rehena Sultana4,
  4. Marcus Tan1,
  5. Joanne HuiMin Quah5,
  6. Baskaran Mani2,3,4,
  7. John C Allen4,
  8. Ching Yu Cheng2,3,4,
  9. Monisha Esther Nongpiur2,3,4,
  10. Tin Aung1,2,3,4
  1. 1Department of Ophthalmology, National University Singapore Yong Loo Lin School of Medicine, Singapore
  2. 2Singapore Eye Research Institute, Singapore
  3. 3Singapore National Eye Centre, Singapore
  4. 4Duke-NUS Medical School, Singapore
  5. 5Outram Polyclinic, SingHealth Polyclinics, Singapore
  1. Correspondence to Dr Monisha Esther Nongpiur, Glaucoma, Singapore Eye Research Institute, Singapore 168751, Singapore; monisha.esther.nongpiur{at}seri.com.sg

Abstract

Purpose To evaluate the performance of swept source optical coherence tomography (SS-OCT) to detect gonioscopic angle closure using different classification algorithms.

Methods This was a cross-sectional study of 2028 subjects without ophthalmic symptoms recruited from a community-based clinic. All subjects underwent gonioscopy and SS-OCT (Casia, Tomey Corporation, Nagoya, Japan) under dark room conditions. For each eye, 8 out of 128 frames (22.5° interval) were selected to measure anterior chamber parameters namely anterior chamber width, depth, area and volume (ACW, ACD, ACA, and ACV), lens vault (LV), iris curvature (IC), iris thickness (IT) from 750 µm and 2000 µm from the scleral spur, iris area and iris volume. Five diagnostic algorithms—stepwise logistic regression, random forest, multivariate adaptive regression splines, recursive partitioning and Naïve Bayes were evaluated for detection of gonioscopic angle closure (defined as ≥2 closed quadrants). The performance of the horizontal frame was compared with that of other meridians.

Results Data from 1988 subjects, including 143 (7.2%) with gonioscopic angle closure, were available for analysis. They were divided into two groups: training (1391, 70%) and validation (597, 30%). The best algorithm for detecting gonioscopic angle closure was stepwise logistic regression with an area under the curve of 0.91 (95% CI 0.88 to 0.93) using all parameters, and 0.88 (95% CI 0.82 to 0.93) using only ACA, LV and IC of the horizontal meridian scan.

Conclusions A stepwise logistic regression model incorporating SS-OCT measurements has a high diagnostic ability to detect gonioscopic angle closure.

  • anterior chamber
  • angle
  • imaging
  • glaucoma

Data availability statement

Data are available on reasonable request.

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Data availability statement

Data are available on reasonable request.

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Footnotes

  • Contributors Design of study (SST, TAT, MEN and TA); conduct of the study (TAT, MT, JHQ, BM, CYC, MEN and TA); collection and management of data (TAT, MT, JHQ and MEN); analysis and Interpretation of data (SST, TAT, RS, JCA, TA and MEN); preparation of manuscript (SST, TAT, RS, TA and MEN); review or approval of manuscript (SST, TAT, RS, MT, JHQ, BM, JCA, CYC, MEN and TA).

  • Funding The study was supported by Biomedical Research Council (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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