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式内窥镜（CE）视频中97%的病理。然而，社区医生和不经常使用者可能会错过高达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。医生及其患者的风险是由于以下原因导致的不太理想的临床结果：1）在视频中遗漏病变/病理，并使患者处于随着时间推移发展更严重状况的风险中，或2）错误地“识别”不存在的病理，从而使患者经历不必要的进一步诊断或外科手术。 本提案中的研究目标将使Ikona能够创建病理优先级图像处理模块。通过实施支持向量机（SVM）和Adaboost方法等现代机器学习技术以及专有图像特征分析，该技术将为特定病理（病变、溃疡、出血等）和胃肠道主要标志（回盲瓣、幽门瓣等）的图像序列中的每帧分配概率度量。将对内窥镜图像数据进行过滤和分类，以便将包含病理的概率最高的图像首先呈现给审查员。 这种病理优先排序并不旨在取代工作流程中的临床医生，而是允许临床医生将更多时间集中在具有更高包含病理可能性的帧上。通常情况下，具有临床意义的病理可能仅存在于单个帧中。50，000帧序列中间的单个“病态”帧很容易被新手审阅者或注意力暂时分散的审阅者忽略。通过我们提出的病理优先化，单个病理帧将在图像序列的开始附近被识别和排序，从而大大增加了审查者检测到的可能性。 特别是对于第一阶段，我们计划研究和开发不同的算法，用于分类图像帧和识别病理和正常帧，以及按病理严重程度对帧进行排名的算法。在实现工作原型之后，我们将进一步测试这些算法在人类临床胶囊内窥镜视频中的临床实用性。 公共卫生相关性：胶囊式内窥镜（CE）广泛用于评估不明原因消化道出血的小肠。经验丰富的胃肠病学家错过了2-3%的病理，部分原因是由于每个CE视频审查50，000帧的疲劳。经验不足的评论者错过了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，并根据其包含病理的概率对其进行排名，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.