Artificial Intelligence for the Management of Colorectal Neoplasia Using Combined-Modality Spectroscopy and Enhanced Imaging

目标：本提案的总体目标是开发、验证和部署人工 智能（AI）为基础的低成本平台，使内镜预防结直肠癌（CRC）更多 高效.我们寻求利用我们在光谱活检工具和自动内窥镜成像方面的工作 解释创建一个准确和广泛采用的实时组织学（RTH）平台， 结合了光学模式和机器学习。目前，结肠镜下CRC的预防取决于 所有息肉的完全切除和组织病理学评估。这一做法导致 大量的息肉，其恶性潜力可以忽略不计。因此，有一个广泛认可的 需要简单、快速和低成本的方法，用于真实的时间内的"智能"息肉评估，以减少活检 成本和风险。为此，以美国胃肠学会为首的主要专业学会 内窥镜检查（ASGE）已经认可了小型息肉的纯光学管理，并提出了 最终采用的指南和可接受的性能阈值（即PIVI声明）。的 过去十年，诊断和治疗结直肠的生物光子技术呈爆炸式增长 更准确地说是肿瘤。虽然已经达到了几个小结肠息肉的PIVI阈值， 由于操作者技能的障碍，在非学术环境中的前瞻性测试已经不足， 体验.机器学习/人工智能的最新进展及其在内窥镜中的应用 成像，已显示出自动RTH克服操作员因素的前景。这种能力将 最终打开了广泛采用节省成本的回收和丢弃的大门 小型息肉的范例。在这方面，我们将建立在我们的工作使用弹性散射 光谱（ESS）活检工具，这已显示出很大的希望RTH，结合它与计算机- 内窥镜图像的辅助诊断（CAD）。我们假设这些新的组合 基于人工智能的互补技术将带来高度准确、破坏性最小和广泛的 结肠直肠息肉的RTH的可展开方法。本项目的具体目标是：1.发展 基于光谱学和内窥镜图像的计算机辅助RTH的AI模型; 2.实施 多站点部署的系统增强和工具设计; 3.执行多中心临床研究，使用 基于ESS和内窥镜图像CAD相结合的AI RTH。 方法：首先，我们将在VA Boston进行一项临床研究，在该研究中，我们将收集ESS 结肠镜检查中息肉的测量和内窥镜图像。我们将使用这些配对数据， 临床特征和组织病理学，以设计和验证计算机辅助RTH的AI算法， 结肠直肠息肉（包括锯齿状病变）利用两种光学信息源。与此同时， 我们将根据一种新的设计， 减少了硬件占用空间和成本。我们还将设计可重复处理的ESS探头并制作原型， 集成到标准息肉切除圈套器中。最后，我们将进行多中心临床研究， 将部署上述工作的其他三个VA设施。的主要终点 目的是分别评价ESS和内镜图像CAD的性能， 组合达到PIVI阈值。作为次要终点，我们将使用临床研究来评估和 改善我们的临床系统。

Objectives: The overarching objective of this proposal is to develop, validate, and deploy an artificial intelligence (AI)-based low-cost platform to make endoscopic prevention of colorectal cancer (CRC) more efficient. We seek to leverage our work in spectroscopic biopsy tools and automated endoscopic imaging interpretation to create an accurate and widely-adoptable, real-time histology (RTH) platform based on combined optical modalities and machine learning. At present, colonoscopic CRC prevention hinges on the complete removal and histopathological assessment of all polyps. This practice results in the removal of large numbers of polyps that have negligible malignant potential. As such, there is a widely-recognized need for simple, rapid, and low-cost methods for “smart” polyp assessment in real time to decrease biopsy costs and risks. To this end, major professional societies, led by The American Society for Gastrointestinal Endoscopy (ASGE), have endorsed the purely optical management of diminutive polyps and have put forth guidelines and acceptable performance thresholds (i.e. the PIVI statements) for eventual adoption. The past decade has seen an explosion in biophotonic technologies toward diagnosing and treating colorectal neoplasia more precisely. While several PIVI thresholds for diminutive colonic polyps have been met, prospective testing in non-academic settings has fallen short due to the barriers of operator skill and experience. Recent advances in machine learning/artificial intelligence, and their application to endoscopic imaging, have shown promise for automating RTH to overcome operator factors. Such capability would finally open the door to widespread adoption of cost-saving resect-and-discard and leave-behind paradigms for diminutive polyps. On this front, we will build on our work using elastic scattering spectroscopy (ESS) biopsy tools, which has shown great promise for RTH, combining it with computer- assisted diagnosis (CAD) of endoscopic images. We hypothesize that the novel combination of these complementary AI based technologies will lead to a highly-accurate, minimally-disruptive, and widely- deployable approach for RTH of colorectal polyps. The specific aims for the present project are: 1. Develop AI models for computer assisted RTH based on spectroscopy and endoscopic images; 2. Implement system enhancements and tool design for multisite deployment; 3. Perform a multisite clinical study using AI-based RTH based on the combination of ESS and CAD of endoscopic images. Methodology: First, we will conduct a clinical study at VA Boston in which we will collect ESS measurements and endoscopic images of polyps at colonoscopy. We will use this paired data, correlated to clinical features and histopathology to design and validate AI algorithms for computer assisted RTH of colorectal polyps (including serrated lesions) that utilize both sources of optical information. Concurrently, we will prototype and build the next-generation ESS system, based on a new design that dramatically reduces the hardware footprint and cost. We will also design and prototype reprocessable ESS probes for integration into standard polypectomy snares. Finally, we will conduct a multisite clinical study involving three other VA facilities where the work described above will be deployed. The primary endpoint of this aim will be to evaluate the performance of ESS and CAD of endoscopic images separately and in combination toward PIVI thresholds. As secondary endpoints, we will use the clinical study to evaluate and improve our clinical systems.