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

2026年7月17日 更新者：Haotian Lin、Zhongshan Ophthalmic Center, Sun Yat-sen University

A Randomized Controlled Trial Comparing the Accuracy of the Postoperative Vision Prediction Model for Cataract Surgery (OCT-PRO) With Clinicians' Predictions of Surgical Outcomes

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.

研究概览

地位

尚未招聘

条件

干预/治疗

详细说明

Cataract is the leading cause of blindness worldwide, yet 5-20% of patients fail to achieve satisfactory visual recovery after surgery. Accurate preoperative prediction remains challenging, particularly for eyes with co-morbid retinal pathologies, as current methods relying on clinician experience and traditional tests (e.g., laser interferometry) often lack reproducibility. Although AI models like OCT-PRO show promise, prospective RCT evidence comparing their accuracy against clinicians is lacking.

This multi-center, randomized, assessor-blinded trial will enroll 534 adults scheduled for cataract surgery. Participants are allocated 1:1 to either the Experimental Group or the Control Group via centralized randomization. In the Experimental Group, clinicians use the OCT-PRO model-integrating OCT images and clinical data-to obtain a predicted postoperative BCVA. Physicians may confirm or adjust this prediction, and the final value is communicated to patients during preoperative counseling. The Control Group receives standard care, where predictions are based solely on conventional clinical assessments without AI assistance. Outcome assessors will be blinded to group allocation.

The primary endpoint is the Mean Absolute Error (MAE) between predicted and actual postoperative BCVA. Secondary endpoints include patient-reported outcomes (expectations, informed choice, satisfaction), clinician acceptance of the model, and correlation analyses. Analysis will follow the Intention-to-Treat principle. This study aims to provide high-level evidence on integrating AI into clinical workflows to enhance prognostic accuracy and optimize shared decision-making in cataract surgery.

研究类型

介入性

注册 (估计的)

534

阶段

  • 不适用

参与标准

研究人员寻找符合特定描述的人，称为资格标准。这些标准的一些例子是一个人的一般健康状况或先前的治疗。

资格标准

适合学习的年龄

  • 成人
  • 年长者

接受健康志愿者

不

描述

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.

学习计划

本节提供研究计划的详细信息，包括研究的设计方式和研究的衡量标准。

研究是如何设计的？

设计细节

  • 主要用途：其他
  • 分配：随机化
  • 介入模型：并行分配
  • 屏蔽：单身的

武器和干预

参与者组/臂
干预/治疗
实验性的：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.
设备：OCT-PRO prediction model
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.
有源比较器：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.
行为的：Routine Preoperative Counseling
Standard preoperative communication based on clinical experience and conventional examinations without AI assistance.

研究衡量的是什么？

主要结果指标

结果测量
大体时间
The Mean Absolute Error (MAE) between the predicted postoperative BCVA and the actual measured BCVA at 1 month post-surgery.
大体时间：Baseline, 1 month post-surgery
Baseline, 1 month post-surgery

次要结果测量

结果测量
措施说明
大体时间
Patient-reported consistency between surgical outcomes and expectations
大体时间：Baseline, 1 month post-surgery
Patient-perceived alignment between actual surgical outcomes and preoperative expectations, measured via a validated questionnaire using a 5-point Likert scale (strongly disagree to strongly agree).
Baseline, 1 month post-surgery
Patient-reported psychological impact of preoperative prognostic disclosure
大体时间：Baseline, 1 month post-surgery
Psychological response to receiving preoperative prognostic information (e.g., anxiety, reassurance), measured via a validated questionnaire using a 5-point Likert scale (strongly disagree to strongly agree).
Baseline, 1 month post-surgery
Patient-reported willingness to recommend prognostic information to others
大体时间：Baseline, 1 month post-surgery
Willingness to recommend cataract surgery prognostic information to others (e.g., family or friends), measured via a validated questionnaire using a 5-point Likert scale (strongly disagree to strongly agree).
Baseline, 1 month post-surgery
Patient-reported satisfaction with healthcare services
大体时间：Baseline, 1 month post-surgery
Patient satisfaction with overall healthcare services received during cataract surgery (e.g., communication, care quality, information clarity), measured via a validated questionnaire using a 5-point Likert scale (strongly disagree to strongly agree).
Baseline, 1 month post-surgery
Clinician-reported outcomes assessing the satisfaction of using OCT-PRO in cataract treatment decision-making
大体时间：Baseline, 1 month post-surgery
Clinician-reported satisfaction with incorporating OCT-PRO into preoperative decision-making (e.g., ease of use, confidence in prediction, communication aid), measured via a validated questionnaire using a 5-point Likert scale (strongly disagree to strongly agree).
Baseline, 1 month post-surgery
Correlation coefficients (Pearson/Spearman) between predicted and actual BCVA in both groups
大体时间：From before surgery to 1 month (±1 week) post-surgery
From before surgery to 1 month (±1 week) post-surgery

合作者和调查者

在这里您可以找到参与这项研究的人员和组织。

赞助

Zhongshan Ophthalmic Center, Sun Yat-sen University

研究记录日期

这些日期跟踪向 ClinicalTrials.gov 提交研究记录和摘要结果的进度。研究记录和报告的结果由国家医学图书馆 (NLM) 审查，以确保它们在发布到公共网站之前符合特定的质量控制标准。

研究主要日期

学习开始 (估计的)

2026年7月20日

初级完成 (估计的)

2026年10月31日

研究完成 (估计的)

2026年12月31日

研究注册日期

首次提交

2026年7月14日

首先提交符合 QC 标准的

2026年7月17日

首次发布 (实际的)

2026年7月20日

研究记录更新

最后更新发布 (实际的)

2026年7月20日

上次提交的符合 QC 标准的更新

2026年7月17日

最后验证

2026年7月1日

更多信息

与本研究相关的术语

关键字

其他相关的 MeSH 术语

其他研究编号

  • 2026KYPJ063

计划个人参与者数据 (IPD)

计划共享个人参与者数据 (IPD)？

不

药物和器械信息、研究文件

研究美国 FDA 监管的药品

不

研究美国 FDA 监管的设备产品

不

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