RT Journal Article
SR Electronic
T1 Anterior segment biometric measurements explain misclassifications by a deep learning classifier for detecting gonioscopic angle closure
JF British Journal of Ophthalmology
JO Br J Ophthalmol
FD BMJ Publishing Group Ltd.
SP 349
OP 354
DO 10.1136/bjophthalmol-2021-319058
VO 107
IS 3
A1 Alice Shen
A1 Michael Chiang
A1 Anmol A Pardeshi
A1 Roberta McKean-Cowdin
A1 Rohit Varma
A1 Benjamin Y Xu
YR 2023
UL http://bjo.bmj.com/content/107/3/349.abstract
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.