OCT-PRO Model vs. Clinicians: Cataract Surgery Outcome Prediction

Cataract is the leading cause of blindness worldwide, yet 5-20% of patients fail to achieve satisfactory visual recovery after surgery. Current methods for predicting postoperative visual acuity lack accuracy, particularly in patients with co-morbid fundus diseases. The OCT-PRO model, developed by our team, uses artificial intelligence (AI) to integrate optical coherence tomography (OCT) images and clinical data to forecast surgical outcomes. This multi-center, randomized, single-blind trial aims to compare the predictive accuracy of OCT-PRO-assisted predictions versus standard clinician predictions. A total of 534 participants will be randomized 1:1 to either the experimental group (OCT-PRO-assisted prediction) or the control group (routine care). The primary outcome is the mean absolute error (MAE) between predicted and actual postoperative best-corrected visual acuity (BCVA). Secondary outcomes include patient satisfaction, informed decision-making scores, and clinician acceptance of the AI tool. This study will provide high-level evidence on the clinical utility of AI in optimizing cataract surgical decision-making and patient communication.

Inclusion Criteria: * Age ≥18 years scheduled to undergo phacoemulsification with intraocular lens (Phaco+IOL) implantation. * Outpatient diagnosis of senile, complicated, or metabolic cataract. * For bilateral cataracts, the eye with more advanced disease will be included. Exclusion Criteria: * History of amblyopia or neuro-ophthalmic disease in the operative eye. * Poor-quality OCT images precluding clear visualization of fundus structures. * Previous intraocular surgery in the operative eye. * Hearing or intellectual impairment preventing adequate cooperation.