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

Sponsor
Zhongshan Ophthalmic Center, Sun Yat-sen University
Study ID
NCT07713069
Status
Not Yet Recruiting

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Conditions

  • Cataract

Eligibility Criteria

Sex
ALL
Age
18 Years - 90 Years
Healthy Volunteers
Not accepted

Interventions

  • OCT-PRO prediction model — DEVICE
    The OCT-PRO model integrates optical coherence tomography (OCT) images and clinical data to predict postoperative best-corrected visual acuity (BCVA). In the experimental group, clinicians input preoperative data into the model, confirm or adjust the prediction, and communicate the final value to patients during preoperative counseling.
  • Routine Preoperative Counseling — BEHAVIORAL
    Standard preoperative communication based on clinical experience and conventional examinations without AI assistance.

Study Details

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 Dates

First listed
Jul 20, 2026
Start date
Jul 20, 2026
Status verified
Jul 2026
Primary completion
Oct 31, 2026
Completion
Dec 31, 2026

Study Design

Enrollment
534 participants (estimated)
Allocation
RANDOMIZED
Intervention model
PARALLEL
Primary purpose
OTHER

Arms

  • Experimental: OCT-PRO Assisted Prediction
    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.
  • Active Comparator: Routine Clinical Prediction
    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.

Primary Outcome Measure

The Mean Absolute Error (MAE) between the predicted postoperative BCVA and the actual measured BCVA at 1 month post-surgery. [ Time Frame: Baseline, 1 month post-surgery ]

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