PT  - JOURNAL ARTICLE
AU  - Alice Shen
AU  - Michael Chiang
AU  - Anmol A Pardeshi
AU  - Roberta McKean-Cowdin
AU  - Rohit Varma
AU  - Benjamin Y Xu
TI  - Anterior segment biometric measurements explain misclassifications by a deep learning classifier for detecting gonioscopic angle closure
AID  - 10.1136/bjophthalmol-2021-319058
DP  - 2023 Mar 01
TA  - British Journal of Ophthalmology
PG  - 349--354
VI  - 107
IP  - 3
4099  - http://bjo.bmj.com/content/107/3/349.short
4100  - http://bjo.bmj.com/content/107/3/349.full
SO  - Br J Ophthalmol2023 Mar 01; 107
AB  - Background/aims To identify biometric parameters that explain misclassifications by a deep learning classifier for detecting gonioscopic angle closure in anterior segment optical coherence tomography (AS-OCT) images.Methods Chinese American Eye Study (CHES) participants underwent gonioscopy and AS-OCT of each angle quadrant. A subset of CHES AS-OCT images were analysed using a deep learning classifier to detect positive angle closure based on manual gonioscopy by a reference human examiner. Parameter measurements were compared between four prediction classes: true positives (TPs), true negatives (TNs), false positives (FPs) and false negatives (FN). Logistic regression models were developed to differentiate between true and false predictions. Performance was assessed using area under the receiver operating curve (AUC) and classifier accuracy metrics.Results 584 images from 127 participants were analysed, yielding 271 TPs, 224 TNs, 77 FPs and 12 FNs. Parameter measurements differed (p<0.001) between prediction classes among anterior segment parameters, including iris curvature (IC) and lens vault (LV), and angle parameters, including angle opening distance (AOD). FP resembled TP more than FN and TN in terms of anterior segment parameters (steeper IC and higher LV), but resembled TN more than TP and FN in terms of angle parameters (wider AOD). Models for detecting FP (AUC=0.752) and FN (AUC=0.838) improved classifier accuracy from 84.8% to 89.0%.Conclusions Misclassifications by an OCT-based deep learning classifier for detecting gonioscopic angle closure are explained by disagreement between anterior segment and angle parameters. This finding could be used to improve classifier performance and highlights differences between gonioscopic and AS-OCT definitions of angle closure.Data are available upon reasonable request. The data that support the findings of this study are available from the corresponding author (BYX), upon reasonable request.