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

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Conditions studied: Cataract

In brief

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.

Key facts

Study ID
NCT07713069
Run by
Zhongshan Ophthalmic Center, Sun Yat-sen University
People needed
534
Starts
2026-07-20
Expected to finish
2026-12-31
Last updated by the study team
2026-07-20

Who can join

Age: 18 and older, up to 90. Sex: any. Healthy volunteers: not accepted.

You may qualify if…

You may not qualify if…

Full record on ClinicalTrials.gov

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