PATHOLOGY MISS RATE RISK REDUCTION IN DIAGNOSTIC SMALL BOWEL CAPSULE ENDOSCOPY

描述(由申请人提供)：训练有素、经验丰富的学术和高容量环境的胃肠病专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人可能会错过高达20%的机会。我们提议的新研究系列的最终目标是开发临床软件，为试图宣布患者没有病理或有特定疾病过程的医生提供自动决策支持。医生和他们的患者面临的风险是，由于以下原因，临床结果不太理想：1)视频中遗漏了一个病变/病理，使患者面临随着时间的推移发展成更严重情况的风险；或2)错误地“识别”了不存在的病理，从而使患者接受不必要的进一步诊断或外科手术。这项提案中的研究目的将使Ikona能够创建一个病理优先图像处理模块。采用现代机器学习技术，如支持向量机(SVM)和Adabost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指定特定病理(病变、溃疡、出血等)和胃肠道主要标志物(回盲瓣、幽门瓣等)的概率度量。将对内窥镜图像数据进行过滤和分类，以便首先将包含病理的概率最高的图像呈现给审查者。这种病理优先排序并不是为了在工作流程中取代临床医生，而是允许临床医生将更多的时间集中在包含病理的可能性更高的帧上。通常情况下，具有临床意义的病理可能只出现在一个单独的画面中。50,000帧序列中间的一个单独的“病态”帧很容易被新手评论者或注意力暂时分散的评论者忽略。通过我们提出的病理优先顺序，单个病理帧将被识别并在图像序列的开头附近进行分类，从而极大地增加了审查者发现的可能性。具体到第一阶段，我们计划研究和开发不同的算法来分类图像帧和识别病理帧和正常帧，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，我们将用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。 公共卫生相关性：胶囊内窥镜(CE)被广泛用于评估不明原因的消化道出血的小肠。经验丰富的胃肠病专家遗漏了2%-3%的病理，部分原因是在审查每个CE视频50,000帧时感到疲劳。经验较少的评审员漏掉了高达20%。我们建议通过开发临床图像处理软件来自动重新排序CE视频帧，根据它们包含病理的概率对它们进行排序，从而降低假阴性的风险。

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.