CATALYZE: A DEEP LEARNING APPROCH FOR CATARACT ASSESSEMENT AND GRADING ON SS-OCT ANTERION IMAGES

PURPOSE: To assess an new objective deep learning model cataract grading method based on Swept-Source Optical Coherence Tomography (SS-OCT) scans provided by the Anterion® (Heidelberg, Germany). SETTING: Single centre study at the Rothschild Foundation, Paris, France. DESIGN: Prospective cross-sectional study METHODS: All patients consulting for cataract evaluation and consenting to study participation were included. History of previous ocular surgery, cornea or retina disorders, and ocular dryness were exclusion criteria. Our CATALYZE pipeline was applied to Anterion® image providing layer-wise cataract metrics and an overall Clinical Significance Index of cataract (CSI). Ocular scatter index (OSI) was also measured with a double-pass aberrometer (OQAS®), and compared to our CSI. RESULTS: Five hundred forty eight eyes were included, 331 in the development set (48 with cataract and 283 controls) and 217 in the validation set (85 with cataract and 132 controls) of 315 patients aged 19-85 years (mean ± SD: 50 ± 21 years). The CSI correlated with the OSI (r 2 = 0.87, P <0.01). CSI area under the ROC curve (AUROC) was comparable to OSI AUROC (0.985 vs 0.981 respectively, P>0.05) with 95% sensitivity and 95% specificity. CONCLUSIONS: Our deep learning pipeline CATALYZE based on Anterion® SS-OCT is a reliable and comprehensive objective cataract grading method.