Prediction of Visual Outcome After Rhegmatogenous Retinal Detachment Surgery Using Artificial Intelligence Techniques.

ABSTRACT

Purpose: This study aimed to develop artificial intelligence models for predicting postoperative functional outcomes in patients with rhegmatogenous retinal detachment (RRD). Methods: A retrospective review and data extraction were conducted on 184 patients diagnosed with RRD who underwent pars plana vitrectomy (PPV) and gas tamponade. The primary outcome was the best-corrected visual acuity (BCVA) at three months after the surgery. Those with a BCVA of less than 6/18 Snellen acuity were classified into a vision impairment group. A deep learning model was developed using presurgical predictors, including ultra-widefield fundus images, structural optical coherence tomography (OCT) images of the macular region, age, gender, and preoperative BCVA. A fusion method was used to capture the interaction between different modalities during model construction. Results: Among the participants, 74 (40%) still had vision impairment after the treatment. There were significant differences in age, gender, presurgical BCVA, intraocular pressure, macular detachment, and extension of retinal detachment between the vision impairment and vision non-impairment groups. The multimodal fusion model achieved a mean area under the curve (AUC) of 0.91, with a mean accuracy of 0.86, sensitivity of 0.94, and specificity of 0.80. Heatmaps revealed that the macular involvement was the most active area, as observed in both the OCT and ultra-widefield images. Conclusions: This pilot study demonstrates that artificial intelligence techniques can achieve a high AUC for predicting functional outcomes after RRD surgery, even with a small sample size. Machine learning methods identified The macular region as the most active region. Translational Relevance Multimodal fusion models have the potential to assist clinicians in predicting postoperative visual outcomes prior to undergoing PPV.