Deep learning detection of retinal detachment: Optical coherence tomography staging and estimation of duration of macular detachment

Objective To test the applicability of deep learning models for detecting and staging rhegmatogenous retinal detachment (RRD) based on morphological features using two- and three-dimensional optical coherence tomography (OCT) scans. Design Retrospective study using deep learning-based image classification analysis of 2D and 3D OCT scans combined with clinical baseline data. Subjects Adult patients presenting to the University Medical Center Hamburg-Eppendorf in Germany. Methods A total of 252 eyes with RRD and 770 control eyes were included. All OCT scans and clinical baseline data were reviewed and graded. Binary and multiclass classification approaches were applied. Main outcome measures Area under the curve (AUC) and precision-recall area under the curve (PR AUC) for detection, stage classification and duration estimation of RRD. Results We employed both statistical and deep learning-based approaches using 2D and 3D OCT data. We evaluated an automated 3D OCT classification model in a multiclass analysis to distinguish RRD scans by macula status from a non-RRD group with macula-on cases (PR AUC = 0.66 ± 0.12, AUC = 0.96 ± 0.01) vs. macula-off cases (PR AUC = 0.86 ± 0.07, 0.98 ± 0.01) against non-RRD cases (PR AUC = 1.00, AUC = 1.00) Furthermore, the 3D model was able to classify the duration of macula-off status (< 3 days) with a PR AUC of 0.68 ± 0.2 and a AUC of 0.97 ± 0.2 when compared to a mixed group including longer macular-off, macular-on and non RRD cases. Lastly, manually graded RRD Stages were correlated with best corrected visual acuity (BCVA), as well as macula-off Duration and classified via a 2D model. A 2D model used for RRD stage classification achieved its best performance for stage 4, with a PR AUC of 0.56 ± 0.11 and an AUC of 0.94 ± 0.02. Conclusion The machine learning models demonstrated strong performance in classifying RRD stages, macula status and duration based on OCT imaging. These findings highlight the potential of deep learning methods to support clinical decision-making and surgical planning in RRD management.