Deep learning based models to study the effect of glaucoma genes on angle dysgenesis in-vivo

ObjectiveTo predict the presence of Angle Dysgenesis on Anterior Segment Optical Coherence Tomography (ADoA) using deep learning and to correlate ADoA with mutations in known glaucoma genes. DesignA cross-sectional observational study. ParticipantsEight hundred, high definition anterior segment optical coherence tomography (ASOCT) B-scans were included, out of which 340 images (One scan per eye) were used to build the machine learning (ML) model and the rest were used for validation of ADoA. Out of 340 images, 170 scans included PCG (n=27), JOAG (n=86) and POAG (n=57) eyes and the rest were controls. The genetic validation dataset consisted of another 393 images of patients with known mutations compared with 320 images of healthy controls MethodsADoA was defined as the absence of Schlemms canal(SC), the presence of extensive hyper-reflectivity over the region of trabecular meshwork or a hyper-reflective membrane (HM) over the region of the trabecular meshwork. Deep learning was used to classify a given ASOCT image as either having angle dysgenesis or not. ADoA was then specifically looked for, on ASOCT images of patients with mutations in the known genes for glaucoma (MYOC, CYP1B1, FOXC1 and LTBP2). Main Outcome measuresUsing Deep learning to identify ADoA in patients with known gene mutations. ResultsOur three optimized deep learning models showed an accuracy > 95%, specificity >97% and sensitivity >96% in detecting angle dysgenesis on ASOCT in the internal test dataset. The area under receiver operating characteristic (AUROC) curve, based on the external validation cohort were 0.91 (95% CI, 0.88 to 0.95), 0.80 (95% CI, 0.75 to 0.86) and 0.86 (95% CI, 0.80 to 0.91) for the three models. Amongst the patients with known gene mutations, ADoA was observed among all the patients with MYOC mutations, as it was also observed among those with CYP1B1, FOXC1 and with LTBP2 mutations compared to only 5% of those healthy controls (with no glaucoma mutations). ConclusionsThree deep learning models were developed for a consensus-based outcome to objectively identify ADoA among glaucoma patients. All patients with MYOC mutations had ADoA as predicted by the models.