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

这一STTR第一阶段项目的广泛/商业影响将开发一种自动化系统，显著提高溃疡性结肠炎(UC)患者疾病严重性评估的速度、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评分的内窥镜部分，临床试验需要由专门的胃肠病专家进行耗时、资源有限的专家阅读过程。拟议的技术将用于多种目的：加快评分过程以确定患者是否符合药物试验的资格，测量基线和疾病变化以获得UC药物试验的FDA终点，以及提供洞察，向GI医生提供有关特定疗法对特定患者的有效性的信息。这一系统在几分钟内进行评分，而不是几天，将提高效率，加快试验参与者的招募和留住(药物试验中最大的挑战)。据估计，每年花费5600万美元用于药物试验的结肠镜检查视频的专家阅读，这项技术不仅可以节省时间，还可以显著降低成本。此外，这种方法将转化为临床护理，为社区医生提供自动化的GI专业知识和对疾病进展和患者对治疗的反应的宝贵洞察力。最后，这项技术可以作为医学生和GI住院医生的教学工具。这个STTR第一阶段项目旨在创建一个自动化系统，用于对UC监测所用的结肠镜检查进行视频评估。这项研究的独特创新因素是自动处理来自结肠镜检查视频的所有数据，以创建对UC疾病负担的可靠、可重复、高效和定量的评估。该方法使用有效的信息量帧分类器、位置估计系统和疾病严重程度分类器的组合来生成整个视频的评分。对临床捕获的视频进行自动化、全面、基于机器学习的评估的算法是该系统的基础。该项目将提高(1)信息性和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质结肠镜视频数据集和来自不同制造商的结肠镜检查数据来验证系统。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不断收集数据来扩展。该项目将使用有效的机器学习方法产生一种新的医疗视频分析方法，以创建用于评估和改进UC CARE的实用的、数据驱动的解决方案。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。

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.