Conditional Diffusion-Based Proxy Representation Recovery for BCVA Prediction with Missing SLO Modality
Abstract: Preoperative prediction of postoperative best-corrected visual acuity (BCVA) can support prognosis assessment and individualized decision-making in cataract surgery. Multimodal learning can integrate preoperative optical coherence tomography (OCT) images, scanning laser ophthalmoscopy (SLO) images, and clinical text to characterize patient status. However, SLO images are often missing in real-world clinical data due to acquisition or data-management issues, resulting in incomplete multimodal inputs. To address this problem, this paper proposes a conditional diffusion-based method for SLO proxy representation recovery and fusion prediction. Instead of reconstructing raw SLO images, the proposed method formulates SLO missingness as proxy representation recovery in the latent feature space. Specifically, OCT image features and clinical text features are first fused to construct an OCT-text conditional representation, which guides the reverse denoising process of the diffusion model to estimate sample-specific SLO proxy representations. The recovered SLO proxy representation is then used as a substitute for the unavailable SLO modality and incorporated into the downstream multimodal fusion prediction network for postoperative BCVA prediction. Experiments were conducted on 2,141 preoperative multimodal cataract samples, with an SLO missing rate of 58.80%. The proposed method achieved an accuracy of 0.9714 under the criterion of an absolute prediction error within 0.1 LogMAR and a mean absolute error of 0.0423, outperforming representative postoperative visual acuity prediction and missing-modality learning methods. These results indicate that representation-level proxy recovery can alleviate the impact of SLO modality missingness on postoperative BCVA prediction.