Development of Artificial Intelligence-Based Approaches for Computer-Aided Management of Colorectal Polyps

背景和目的：结直肠癌(CRC)是世界上第二大癌症死亡原因。 美国每年新增病例近15万例，死亡5万人。结肠镜检查伴息肉切除术 仍然是结直肠癌筛查和监测的黄金标准，因为在 结肠镜检查可以改变疾病的结果，并为后续的治疗提供信息。标准做法 继续赞成切除所有可见的息肉进行组织病理学评估，尽管估计 近一半的息肉是非肿瘤性的。研究表明，可靠地预测息肉的能力 实时内窥镜病理检查可大幅提高成本效益 结直肠癌的结肠镜检查。进行结肠镜检查的次数正在增加，在退伍军人事务部，超过 在五年内翻了一番。此需求不包括约30%所需的后续程序 对病人进行筛查。因此，结肠镜检查可以从各个层面的效率改进中受益匪浅。在……里面 有鉴于此，在过去的十年里，内窥镜技术取得了爆炸性的进展， 更准确地诊断和治疗结直肠肿瘤。人工智能(AI)的最新进展， 特别是在深度学习领域，以及它们在内窥镜成像中的应用，已经显示出希望 用于自动化内窥镜息肉病理预测，克服基于操作员的息肉病理 评估因素，如观察者之间的可变性、技能和经验。这样的能力最终会 为广泛采用节约成本的切除-丢弃和留下范例打开大门 由美国胃肠道内窥镜保存和治疗学会提出的小息肉 纳入有价值的内窥镜创新指南。更重要的是，人工智能的融入- 将定量图像解释应用到临床实践中，包括在退伍军人事务部，有可能 增加癌症的早期检测，从而降低患者的发病率和死亡率。为此，主要目标是 建议的研究的目的是利用人工智能，特别是深度学习模型，来开发准确和 强大的计算机辅助诊断(CADx)平台，支持纯内窥镜、光学评估 粘膜病理学，特别是大肠息肉。同时，使用人工智能模型评估结肠 黏膜和管腔特征已知的通知结肠镜检查质量将被调查。 方法：本研究将以三个目标为指导。目标1：用于预测息肉的稳健分类模型 病理学将会得到发展。来自现有数据集和临床记录的标记图像和临床数据， 将用于设计和验证深度学习模型。设计将由两个步骤组成：勾勒 包含息肉的图像中的区域，以及随后对息肉区域的分析以提供病理 预测。借用增强现实的方面，病理预测以及估计 息肉边界，将以直观和临床友好的方式呈现给内窥镜医生，作为一种伪 颜色叠加，增强了模型输出预测的透明度和可解释性。这 因此，即时视觉反馈可以为结肠镜检查中的临床决策提供信息。目标2将专注于使用 与结直肠肿瘤相关的临床危险因素与内窥镜成像数据相结合 加强对息肉病理的预测。目的是调查公认的结直肠癌临床的纳入情况。 在我们的息肉病理学中，从患者的电子健康记录中获得的风险因素和生物标志物 预测深度学习模型。最后，在目标3中，基于深度学习的检测、分割和 目标1中制定的分类框架将适用于对肠道准备进行评分，识别 盲肠标志和直肠屈曲，识别结肠憩室，描绘内窥镜纹身 标记。这些特征与执行高质量的结肠镜检查有关，为此自动 鉴定可以改善和促进内窥镜检查结果的记录和报告的生成。

Background and Objectives: Colorectal cancer (CRC) is the second leading cause of cancer death in the United States, with nearly 150,000 new cases and 50,000 deaths annually. Colonoscopy with polypectomy remains the gold standard for CRC screening and surveillance since removal of neoplastic polyps during colonoscopy modifies disease outcomes and informs subsequent management. Standard practice continues to favor removal of all visualized polyps for histopathological assessment, despite estimates that nearly half of the polyps are non-neoplastic. Studies have shown that the capability to reliably predict polyp pathology endoscopically in real time could result in substantial improvement in the cost-effectiveness of colonoscopy for CRC. The number of colonoscopies performed is increasing, and in the VA more than doubled in a five-year span. This demand does not include subsequent procedures required in ~30% of screened patients. Thus, colonoscopy can benefit greatly from efficiency improvements at every level. In light of this, the past decade has seen an explosion in advances in endoscopic technologies toward diagnosing and treating colorectal neoplasia more precisely. Recent advances in artificial intelligence (AI), specifically in the field of deep learning, and their application to endoscopic imaging, have shown promise for automating endoscopic polyp pathology predictions, overcoming operator-based polyp pathology assessment factors such as interobserver variability, skill, and experience. Such capability would finally open the door to widespread adoption of cost-saving resect-and-discard and leave-behind paradigms for diminutive polyps, as proposed by the American Society for Gastrointestinal Endoscopy Preservation and Incorporation of Valuable Endoscopic Innovations guidelines. More importantly, the incorporation of AI- based quantitative image interpretation into clinical practice, including in the VA, has the potential to increase early cancer detection thus reducing patient morbidity and mortality. To this end, the main goal of the proposed study is to leverage AI, specifically deep learning models, to develop an accurate and robust computer aided diagnosis (CADx) platform to enable the purely endoscopic, optical assessment of mucosal pathologies, specifically colorectal polyps. In parallel, the use of AI models to assess colonic mucosal and luminal features known to inform colonoscopy quality will be investigated. Methods: The study will be guided by three aims. In Aim 1 robust classification models for predicting polyp pathology will be developed. Labeled images and clinical data, from existing datasets and clinical records, will be used to design and validate deep learning models. The design will consist of two steps: outlining regions in an image containing a polyp, and subsequent analysis of the polyp region to provide a pathology prediction. Borrowing from aspects of augmented reality, the pathology prediction along with the estimated polyp boundary, will be presented to endoscopists in an intuitive and clinically friendly manner as a pseudo- color overlay, enhancing the transparency and interpretability of the models output predictions. This immediate visual feedback can thus inform clinical decisions during colonoscopy. Aim 2 will focus on using clinical risk factors associated with colorectal neoplasia in combination with endoscopic imaging data to enhance predictions of polyp pathology. The goal is to investigate incorporation of recognized CRC clinical risk factors and biomarkers, obtained from patients’ electronic health records, in our polyp pathology prediction deep learning models. Finally, in Aim 3 the deep learning-based detection, segmentation, and classification frameworks developed in Aim 1 will be adapted for scoring bowel preparation, recognizing cecal landmarks and rectal retroflexion, identifying colonic diverticula, and delineating endoscopic tattoo markings. These features are associated with performing of high-quality colonoscopy, for which automated identification could improve and facilitate documentation of endoscopic findings and report generation.