Multimodal Cataract Severity Classification from AS-OCT Examination Reports: Analyzing the Complementarity of B-Scan Images and A-Scan Profiles

Background/Objectives: Deep learning on anterior segment optical coherence tomography (AS-OCT) has been applied mainly to nuclear cataract. This study evaluated whether the A-scan profile in Anterion reports adds information to the B-scan image for classifying nuclear (N), cortical (C), and posterior subcapsular (P) cataract severity. Methods: B-scan images and A-scan profiles were extracted from 1416 reports of 691 patients. LOCS III grades were converted into three levels (None/Mild, Moderate, Advanced); inter-observer agreement was excellent (weighted κ 0.84–0.97). Image and profile encoders were combined using five fusion strategies and evaluated with patient-level 5-fold cross-validation. Results: Adding the A-scan profile improved F1 for N (+0.030) and P (+0.094) but not for C (−0.002). All fusion strategies outperformed single-modality models and performed comparably (average F1 0.514–0.531). For P, the gain was largest at the Moderate grade (F1 0.183 to 0.336), and masking the profile segment containing the posterior capsule caused the largest drop. B-scan line profiles could not replace the device-provided profile, and most misclassifications occurred between adjacent grades. Conclusions: The A-scan profile provided a useful incremental contribution, particularly for N and P, in this single-center dataset. The approach may be useful in retrospective and multi-center settings, but this remains to be demonstrated.