Advancing Ulcerative Colitis Monitoring with Deep Learning Models

利用深度学习模型推进溃疡性结肠炎监测

• 批准号: 10081185
• 负责人: Robert Michael Cheetham
• 金额: $ 15万
• 依托单位: AZAVEA, INC
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-09-01 至 2021-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10081185
• 关键词: Address; Algorithms; Appearance; Architecture; Catalogs; Clinical Data; Clinical Research; Clinical Trials; Communication; Computer software; Country; Coupled; Data; Data Set; Detection; Development; Diagnosis; Diagnostic; Disease; Disease Progression; Eligibility Determination; Endoscopy; Endpoint Determination; Ensure; Evaluation; Foundations; Future; Gastrointestinal Diseases; Histologic; Histology; Histopathology; Hospitals; Image; Image Analysis; Imagery; Individual; Knowledge; Label; Learning Skill; Machine Learning; Manuals; Maps; Measures; Medical; Medical Imaging; Metadata; Methods; Microscope; Modeling; Monitor; Output; Pathologist; Pathology; Patients; Pharmaceutical Preparations; Phase; Play; Predictive Value; Process; Publications; Readability; Reader; Reporting; Research; Role; Series; Services; Severity of illness; Site; Software Design; Speed; Stains; System; Techniques; Technology; Testing; Time; Tissues; Training; Translating; Triage; Ulcerative Colitis; Validation; base; deep learning; deep learning algorithm; diagnostic accuracy; digital imaging; gastrointestinal; imaging biomarker; imaging detection; improved; indexing; insight; instrument; learning network; prototype; software development; tool; treatment response

Project Summary/Abstract The number of practicing pathologists around the world is expected to decrease by as much as 30% over the next two decades, with some of the world’s poorest countries having a ratio of only one pathologist to many hundreds of thousands of people. At the same time, the diagnostic caseload that requires their expertise in clinical trials and hospital settings will continue to grow. The digitization of pathology data, coupled with the use of machine learning techniques for analyzing and scoring the data, provides exciting opportunities to make the field of pathology more efficient and scalable, even as the workforce continues to evolve. Deep learning in particular provides the potential to enhance the interpretation of medical images by improving the detection of image-based biomarkers for a broad range of diseases. Image interpretation plays an important role in patient eligibility and endpoint determination during the course of clinical trials. For patients with ulcerative colitis, the development of trained and reliable algorithms that can help pathologists identify disease progression and response to treatment in a timely and effective manner can provide benefit in two important ways. First, it will help to ensure that the most appropriate score for histological disease severity is being assigned to each image using the Robarts Histopathology Index (RHI) or similar grading scale. Second, it will support a triage process by which images known to contain non- healthy tissues can be prioritized for earlier assessment. Through a unique partnership between Azavea, a geospatial technology and machine learning firm, and Robarts, a clinical trials organization, the proposed research will begin to address these needs by developing deep learning algorithms for histopathology digital image analysis, testing them on machine-readable annotations of medical imagery from previous clinical studies, and exposing them through a metadata- searchable interface that will enable the images to be categorized and quickly accessed by pathologists and others to support reader training and increase communication between multiple readers and sites. In so doing, it will not only help streamline the evaluation of new ulcerative colitis treatments that rely heavily on the image interpretation process, but also provide the foundation for the identification of additional components present in other gastrointestinal disease indications in the future.

项目总结/摘要 世界各地执业病理学家的数量预计将减少多达30%， 未来二十年，世界上一些最贫穷的国家的病理学家比例只有一比多， 成千上万的人。与此同时，需要他们在以下方面的专门知识的诊断案件量 临床试验和医院设置将继续增长。病理学数据的数字化， 使用机器学习技术来分析和评分数据，提供了令人兴奋的机会， 病理学领域更高效和可扩展，即使劳动力不断发展。深度学习在 特别是提供了通过改善对医学图像的检测来增强对医学图像的解释的潜力。 基于图像的生物标志物，用于广泛的疾病。 图像判读在患者合格性和终点确定中起着重要作用， 临床试验的过程。对于溃疡性结肠炎患者， 这可以帮助病理学家及时有效地识别疾病进展和治疗反应， 方式可以在两个重要方面提供益处。首先，它将有助于确保最适当的分数 使用Robarts组织学指数（RHI）将组织学疾病严重程度分配给每张图像 或类似的分级标准。其次，它将支持一个分类过程，通过该过程，已知包含非 健康组织可以优先用于早期评估。 通过地理空间技术和机器学习公司Azavea与 Robarts，一个临床试验组织，拟议的研究将开始，以解决这些需求， 用于组织病理学数字图像分析的深度学习算法，在机器可读的 从以前的临床研究的医学图像的注释，并通过元数据暴露它们- 可搜索的界面，使图像能够分类，并迅速访问病理学家， 另一些用于支持读者培训，并增加多个读者和网站之间的沟通。这样 这样做，它不仅有助于简化对严重依赖药物的新溃疡性结肠炎治疗的评估， 图像判读过程，也为识别附加成分提供基础 在未来的其他胃肠道疾病的适应症。