Multimodal Cataract Severity Classification from AS-OCT Examination Reports: Analyzing the Complementarity of B-Scan...

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.