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

2026년 7월 17일 업데이트: 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.

연구 개요

상태

아직 모집하지 않음

정황

상세 설명

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.

연구 유형

중재적

등록 (추정된)

534

단계

  • 해당 없음

참여기준

연구원은 적격성 기준이라는 특정 설명에 맞는 사람을 찾습니다. 이러한 기준의 몇 가지 예는 개인의 일반적인 건강 상태 또는 이전 치료입니다.

자격 기준

공부할 수 있는 나이

  • 성인
  • 고령자

건강한 자원 봉사자를 받아들입니다

아니

설명

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.

공부 계획

이 섹션에서는 연구 설계 방법과 연구가 측정하는 내용을 포함하여 연구 계획에 대한 세부 정보를 제공합니다.

연구는 어떻게 설계됩니까?

디자인 세부사항

  • 주 목적: 다른
  • 할당: 무작위
  • 중재 모델: 병렬 할당
  • 마스킹: 하나의

무기와 개입

참가자 그룹 / 팔
개입 / 치료
실험적: 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.
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.
활성 비교기: 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.
Standard preoperative communication based on clinical experience and conventional examinations without AI assistance.

연구는 무엇을 측정합니까?

주요 결과 측정

결과 측정
기간
The Mean Absolute Error (MAE) between the predicted postoperative BCVA and the actual measured BCVA at 1 month post-surgery.
기간: Baseline, 1 month post-surgery
Baseline, 1 month post-surgery

2차 결과 측정

결과 측정
측정값 설명
기간
Patient-reported consistency between surgical outcomes and expectations
기간: 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
기간: 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
기간: 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
기간: 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
기간: 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
기간: From before surgery to 1 month (±1 week) post-surgery
From before surgery to 1 month (±1 week) post-surgery

공동 작업자 및 조사자

여기에서 이 연구와 관련된 사람과 조직을 찾을 수 있습니다.

연구 기록 날짜

이 날짜는 ClinicalTrials.gov에 대한 연구 기록 및 요약 결과 제출의 진행 상황을 추적합니다. 연구 기록 및 보고된 결과는 공개 웹사이트에 게시되기 전에 특정 품질 관리 기준을 충족하는지 확인하기 위해 국립 의학 도서관(NLM)에서 검토합니다.

연구 주요 날짜

연구 시작 (추정된)

2026년 7월 20일

기본 완료 (추정된)

2026년 10월 31일

연구 완료 (추정된)

2026년 12월 31일

연구 등록 날짜

최초 제출

2026년 7월 14일

QC 기준을 충족하는 최초 제출

2026년 7월 17일

처음 게시됨 (실제)

2026년 7월 20일

연구 기록 업데이트

마지막 업데이트 게시됨 (실제)

2026년 7월 20일

QC 기준을 충족하는 마지막 업데이트 제출

2026년 7월 17일

마지막으로 확인됨

2026년 7월 1일

추가 정보

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아니요

약물 및 장치 정보, 연구 문서

미국 FDA 규제 의약품 연구

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미국 FDA 규제 기기 제품 연구

아니

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OCT-PRO prediction model에 대한 임상 시험

구독하다