PATHOLOGY MISS RATE RISK REDUCTION IN DIAGNOSTIC SMALL BOWEL CAPSULE ENDOSCOPY

描述（由申请人提供）：在学术和高容量环境中训练有素，经验丰富的胃肠病学家可以可靠地识别胶囊内窥镜（CE）视频中97%的病理。然而，社区医生和不经常使用的用户可能会遗漏高达20%。我们提出的新研究路线的最终目标是开发临床软件，为试图宣布患者无病理或具有某种疾病过程的医生提供自动决策支持。对于医生和他们的病人来说，风险是由于以下原因导致的不理想的临床结果：1)在视频中遗漏病变/病理，使病人有可能随着时间的推移发展成更严重的疾病，或者2)错误地“识别”不存在的病理，从而使病人接受不必要的进一步诊断或手术。本提案的研究目标将使Ikona能够创建一个病理优先级图像处理模块。采用现代机器学习技术，如支持向量机（SVM）和Adaboost方法以及专有的图像特征分析，该技术将为图像序列中的每一帧分配一个概率度量，用于特定病理（病变，溃疡，出血等）和胃肠道中的主要标志（回盲肠阀，幽门阀等）。对内窥镜图像数据进行过滤和分类，使包含病理的可能性最大的图像首先呈现给审稿人。这种病理优先排序并不是为了取代临床医生的工作流程，而是为了让临床医生把更多的时间集中在更有可能包含病理的框架上。通常情况下，临床意义重大的病理可能只出现在一个框架中。在5万帧序列中的单个“病态”帧很容易被新手审稿人或注意力暂时分散的审稿人所忽视。根据我们提出的病理优先级排序，单个病理帧将在图像序列的开始附近被识别和排序，从而大大增加审稿人发现的可能性。特别是在第一阶段，我们计划研究和开发不同的算法来分类图像帧并识别病理和正常帧，以及根据病理严重程度对帧进行排序的算法。随着工作原型的实现，我们将通过人类临床胶囊内窥镜视频进一步测试这些算法的临床实用性。

DESCRIPTION (provided by applicant): Well trained, experienced gastroenterologists in academic and high volume settings can reliably recognize 97% of pathologies in Capsule Endoscopy (CE) video. However, community physicians and infrequent users may miss up to 20%. The end goal of our proposed new line of research is to develop clinical software that provides automatic decision support to physicians who are trying to declare that a patient is pathology free or has a certain disease process. The risk for the physician - and their patients - is that of a less than optimal clinical outcome due to: 1) missing a lesion/pathology in the video and putting the patient at risk of developing a more serious condition over time, or 2) mistakenly "identifying" a pathology that is not present and thus subjecting the patient to unnecessary further diagnostic or surgical procedures. The research aims in this proposal will enable Ikona to create a pathology prioritization image processing module. Implementing modern machine learning techniques such as Support Vector Machines (SVM) and Adaboost methodologies together with proprietary image feature analysis, this technology will assign a probability metric to every frame in the image sequence for specific pathology (lesions, ulcers, bleeding, etc) and the major landmarks in the GI tract (ileo-cecal valve, pyloric valve etc.). Filtering and sorting endoscopy image data will be done such that the images with the highest probability of containing pathology will be presented to the reviewer first. This pathology prioritized sequencing is not intended to replace the clinician in the workflow, but rather to allow the clinician to focus more time on frames with a higher potential of containing pathology. Often times, clinically significant pathology may only be present in a single frame. A single "pathological" frame in the middle of a 50,000 frame sequence can easily be overlooked by a novice reviewer or a reviewer whose attention is temporarily distracted. With our proposed pathology prioritization, that single pathological frame will be identified and sorted near the beginning of the image sequence thus greatly increasing the likelihood of detection by the reviewer. Specifically for Phase I, we plan to investigate and develop different algorithms for classifying image frames and recognizing pathological and normal frames, and, algorithms for ranking frames by severity of pathology. Following the implementation of a working prototype, we will further test the clinical utility of these algorithms with human clinical capsule endoscopy videos. PUBLIC HEALTH RELEVANCE: Capsule Endoscopy (CE) is widely used for assessing the small intestine in obscure gastrointestinal bleeding. Experienced gastroenterologists miss 2-3% of pathologies in part due to fatigue from reviewing 50,000 frames per CE video. Less experienced reviewers miss up to 20%. We propose to reduce the risk of false negatives by developing clinical image processing software to automatically re-order the CE video frames, ranking them by the probability they contain pathology.