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:
Exclusion Criteria:
Clinicians input preoperative OCT images and clinical data into the OCT-PRO model to generate a predicted postoperative BCVA. Physicians may confirm or adjust this AI prediction to determine a final value. This final prediction value is then communicated to the patient as supplementary information during routine preoperative counseling.
Clinicians perform standard preoperative assessments based on clinical experience and examination results. Predictions of postoperative visual acuity are made solely by physician judgment without AI assistance. Patients receive routine preoperative counseling regarding surgical risks and expected outcomes.
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