STTR Phase I: A Fully-Automated Endoscopic Scoring System for Ulcerative Colitis

STTR 第一阶段：溃疡性结肠炎全自动内窥镜评分系统

• 批准号: 1938390
• 负责人: John Feltz
• 金额: $ 22.5万
• 依托单位: PREHAB TECHNOLOGIES, LLC
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-02-01 至 2020-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1938390&HistoricalAwards=false
• 关键词: STTR; Phase; Fully; Automated; Endoscopic

The broader/commercial impact of this STTR Phase 1 project will develop an automated system that significantly increases the speed, reliability, and accuracy of disease severity assessment in ulcerative colitis (UC) patients. This lifelong debilitating disease impacts almost 1 M US patients, and developing effective treatments requires accurate, reliable, and timely scoring. Currently, the FDA-approved primary diagnostic is the endoscopic component of the Mayo score, and clinical trials require a time-consuming, resource-constrained process of expert reading by specialized gastroenterologists. The proposed technology will serve multiple purposes: expediting the scoring process to determine patient eligibility for drug trials, measuring baseline and disease change to obtain FDA endpoints for UC drug trials, and providing insights that inform GI physicians on the effectiveness of a particular therapy for a specific patient. This system scoring – performed in minutes, rather than days – will improve efficiency and expedite recruitment and retention of trial participants (the greatest challenge in drug trials). With an estimated $56 M spent annually on expert reading of colonoscopy videos for drug trials, this technology would not only save time but would also significantly reduce costs. In addition, this methodology will translate into clinical care, providing community physicians with automated GI expertise and valuable insight into disease progression and patient response to therapy. Finally, this technology could be utilized as a teaching tool for medical students and GI residents. This STTR Phase I project is designed to create an automated system for video assessment of colonoscopies taken for UC monitoring. The unique innovative factor in this research is the automated processing of all data available from colonoscopy videos to create a reliable, repeatable, efficient, and quantitative assessment of the burden of UC disease. The approach uses a combination of an effective informative frame classifier, location estimation system, and disease severity classifier to generate scoring of the entire video. Algorithms for automated, comprehensive, machine-learning-based assessment of clinically-captured videos are the foundation of the system. The project will improve (1) classification accuracy between informative vs. non-informative video frames, (2) estimation of the camera location, and (3) validate the system against a heterogenous colonoscopy video dataset from multiple clinical providers and from colonoscopes from various manufacturers. The algorithms will be optimized for an endoscopic assessment and scoring system and extended through ongoing data collection with academic partners. This project will result in a novel approach to medical video analysis using effective machine learning methods to create a practical, data-driven solution for assessment and improvement of UC care.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

STTR 第一阶段项目的更广泛/商业影响将开发一种自动化系统，显着提高溃疡性结肠炎 (UC) 患者疾病严重程度评估的速度、可靠性和准确性，这种终生衰弱的疾病影响着近 100 万美国患者。开发有效的治疗方法需要准确、可靠和及时的评分，目前 FDA 批准的主要诊断是 Mayo 评分的内窥镜组成部分，而临床试验需要由专业胃肠病学家进行耗时且资源有限的专家阅读过程。该技术将服务于多种目的：加快评分过程以确定患者是否有资格进行药物试验，测量基线和疾病变化以获得 UC 药物试验的 FDA 终点，并提供见解，让胃肠道医生了解特定患者的特定疗法的有效性该系统评分（只需几分钟而不是几天）将提高效率并加快试验参与者的招募和保留（药物试验中最大的挑战），估计每年花费 5600 万美元用于专家阅读药物试验的结肠镜检查视频。技术不仅可以节省时间此外，这种方法还将显着降低成本，为社区医生提供自动化胃肠道专业知识以及对疾病进展和患者对治疗反应的宝贵见解。最后，该技术可以用作医学教学工具。该 STTR 第一阶段项目旨在创建一个用于 UC 监测的结肠镜检查视频评估的自动化系统，该研究的独特创新因素是自动处理结肠镜检查视频中的所有数据，以创建可靠的、可重复、高效、定量的负担评估该方法结合使用有效的帧分类器、位置估计系统和疾病严重程度分类器来生成整个视频的评分算法，用于对临床捕获的视频进行自动、全面、基于机器学习的评估。该项目将提高 (1) 信息性视频帧与非信息性视频帧之间的分类准确性，(2) 摄像机位置的估计，以及 (3) 根据来自多个临床提供商的异构结肠镜检查视频数据集验证系统。和结肠镜检查这些算法将针对内窥镜评估和评分系统进行优化，并通过与学术合作伙伴的持续数据收集进行扩展，该项目将使用有效的机器学习方法来创建实用的数据分析方法。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。