OphthoACR (Ophthalmology Automated Chart Review): An AI-Powered Tool for Complete Automation of Ophthalmology Chart Reviews and Cohort Data Analysis.

IF 2.6 3区医学 Q2 OPHTHALMOLOGY

Translational Vision Science & Technology Pub Date : 2025-10-01 DOI:10.1167/tvst.14.10.8

Karen M Chen, Kevin W Chen, Vlad Diaconita, Stanley Chang, Leejee H Suh

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引用次数: 0

Abstract

Purpose: Retrospective chart reviews in ophthalmology are essential for gaining clinical insights, but they remain labor-intensive and prone to error. Despite digitization through electronic health records, extracting and interpreting lengthy, unstructured patient histories remains challenging, particularly in ophthalmology, which relies heavily on both imaging and text-based reports. We introduce OphthoACR, a Health Insurance Portability and Accountability Act-compliant artificial intelligence (AI)-powered tool for automated chart review and cohort analyses in ophthalmology.

Methods: OphthoACR was applied to extract 16 variables of increasing task difficulty from the complete chart histories of 91 patients who underwent secondary intraocular lens surgery at the Columbia University Irving Medical Center from January 2020 to August 2024, for a total of 5834 unique documents. The tool integrates a fine-tuned large language model into a robust pipeline to extract and contextualize unstructured clinical data, including operative reports and imaging documents. OphthoACR's performance was compared to manual and AI-assisted chart reviews.

Results: OphthoACR achieved 94% accuracy in extracting variables of interest, significantly outperforming manual review (83%). It demonstrated 97% specificity, 92% sensitivity, and a Cohen's κ of 0.70, indicating robust agreement. Average time for OphthoACR to process a patient chart was 80 seconds, a 95% reduction compared to the manual review's average of 25.2 minutes. For cohort-wide processing, the improvement was 99.9% due to parallel processing of patients' charts.

Conclusions: OphthoACR significantly improves the accuracy and efficiency of ophthalmology chart reviews, offering an unprecedented automated solution to analyze large patient cohorts.

Translational relevance: OphthoACR provides end-to-end automation of retrospective chart reviews, transforming the currently labor-intensive manual process into an efficient, accurate, and scalable solution that substantially enhances clinical research.

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OphthoACR（眼科自动图表审查）：一个人工智能驱动的工具，完全自动化眼科图表审查和队列数据分析。

目的：眼科回顾性图表回顾对于获得临床见解是必不可少的，但它们仍然是劳动密集型的，容易出错。尽管通过电子健康记录实现了数字化，但提取和解释冗长、非结构化的患者病史仍然具有挑战性，特别是在严重依赖影像和基于文本的报告的眼科。我们介绍OphthoACR，一个符合健康保险流通与责任法案的人工智能（AI）驱动的工具，用于眼科的自动图表审查和队列分析。方法：应用OphthoACR从2020年1月至2024年8月在哥伦比亚大学欧文医学中心接受二次人工晶状体手术的91例患者的完整病历中提取16个任务难度增加的变量，共5834个独特文件。该工具将一个经过微调的大型语言模型集成到一个强大的管道中，以提取和上下文化非结构化临床数据，包括手术报告和成像文档。将OphthoACR的性能与人工和人工智能辅助的图表审查进行比较。结果：OphthoACR在提取感兴趣变量方面达到94%的准确率，显著优于人工评价（83%）。其特异性为97%，敏感性为92%，Cohen's κ为0.70，表明具有很强的一致性。OphthoACR处理患者病历的平均时间为80秒，与人工检查的平均时间25.2分钟相比减少了95%。对于全队列处理，由于并行处理患者图表，改善率为99.9%。结论：OphthoACR显著提高了眼科病历审查的准确性和效率，为分析大型患者队列提供了前所未有的自动化解决方案。翻译相关性：OphthoACR提供回顾性图表审查的端到端自动化，将当前劳动密集型的手动过程转变为高效、准确和可扩展的解决方案，从而大大增强了临床研究。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Translational Vision Science & Technology Engineering-Biomedical Engineering

CiteScore

5.70

自引率

3.30%

发文量

346

审稿时长

25 weeks

期刊介绍： Translational Vision Science & Technology (TVST), an official journal of the Association for Research in Vision and Ophthalmology (ARVO), an international organization whose purpose is to advance research worldwide into understanding the visual system and preventing, treating and curing its disorders, is an online, open access, peer-reviewed journal emphasizing multidisciplinary research that bridges the gap between basic research and clinical care. A highly qualified and diverse group of Associate Editors and Editorial Board Members is led by Editor-in-Chief Marco Zarbin, MD, PhD, FARVO. The journal covers a broad spectrum of work, including but not limited to: Applications of stem cell technology for regenerative medicine, Development of new animal models of human diseases, Tissue bioengineering, Chemical engineering to improve virus-based gene delivery, Nanotechnology for drug delivery, Design and synthesis of artificial extracellular matrices, Development of a true microsurgical operating environment, Refining data analysis algorithms to improve in vivo imaging technology, Results of Phase 1 clinical trials, Reverse translational ("bedside to bench") research. TVST seeks manuscripts from scientists and clinicians with diverse backgrounds ranging from basic chemistry to ophthalmic surgery that will advance or change the way we understand and/or treat vision-threatening diseases. TVST encourages the use of color, multimedia, hyperlinks, program code and other digital enhancements.