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

使用组合模态光谱和增强成像的人工智能治疗结直肠肿瘤

• 批准号: 9889313
• 负责人: SATISH K SINGH
• 金额: --
• 依托单位: VA BOSTON HEALTH CARE SYSTEM
• 依托单位国家: 美国
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-07-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9889313
• 关键词: Adopted; Adoption; Algorithm Design; American; Artificial Intelligence; Benchmarking; Benign; Biophotonics; Biopsy; Boston; Calibration; Clinical; Clinical Research; Colonic Polyps; Colonoscopy; Colorectal Neoplasms; Colorectal Polyp; Computational algorithm; Computer Assisted; Computer Models; Computer software; Computer-Assisted Diagnosis; Computers; Cost Savings; Data; Diagnosis; Elastic scattering spectroscopy; Endoscopes; Endoscopy; Ensure; Environment; Excision; Explosion; Feedback; Fluorescence; Forcep; Gastrointestinal Endoscopy; Guidelines; Healthcare; Healthcare Systems; Histology; Histopathology; Image; Image Analysis; Image Enhancement; In Situ; Jamaica; Lesion; Light; Machine Learning; Malignant - descriptor; Measurement; Methodology; Methods; Modality; Multi-site clinical study; Optics; Performance; Polypectomy; Polyps; Precancerous Polyp; Procedures; Professional Organizations; Reporting; Reproducibility; Research; Risk; Site; Societies; Source; Spectrum Analysis; Standardization; System; Technology; Time; Tissues; Variant; Work; base; cancer risk; chromoscopy; colorectal cancer prevention; cost; design; experience; improved; instrument; instrumentation; microendoscopy; miniaturize; next generation; novel; primary endpoint; prospective test; prototype; secondary endpoint; skills; tool; user-friendly

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.

目标：该提案的总体目标是开发、验证和部署人工 基于智能(AI)的低成本平台，使结直肠癌(CRC)的内窥镜预防更 效率很高。我们寻求利用我们在光谱活检工具和自动化内窥镜成像方面的工作 解释以创建一个准确且可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，结肠镜下结直肠癌的预防取决于 对所有息肉进行彻底切除和组织病理学评估。这种做法会导致删除 大量息肉的恶性潜能可以忽略不计。因此，有一个广为人知的 需要简单、快速和低成本的方法来进行实时的智能息肉评估，以减少活检 成本和风险。为此，以美国胃肠道协会为首的主要专业协会 内窥镜(ASGE)，支持对微小息肉的纯光学治疗，并提出了 最终采用的指导方针和可接受的绩效门槛(即PIVI声明)。这个 在过去的十年里，生物光子学技术在诊断和治疗结直肠癌方面取得了爆炸性的进展。 更准确地说是肿瘤。虽然已经达到了几个微小结肠息肉的Pivi阈值， 由于操作员技能和技能的障碍，非学术环境中的预期测试还不够理想 经验。机器学习/人工智能的新进展及其在内窥镜检查中的应用 成像，已经显示出自动RTH的前景，以克服操作员因素。这样的能力将 最后，为广泛采用节约成本的切除、丢弃和遗留技术打开大门 小息肉的范例。在这方面，我们将在我们的工作的基础上使用弹性散射 光谱学(ESS)活组织检查工具，已显示出RTH的巨大前景，将其与计算机相结合- 内窥镜图像的辅助诊断(CAD)。我们假设这两者的新奇组合 基于人工智能的互补技术将带来高度准确、最小干扰和广泛的- 可部署的大肠息肉RTH治疗方法。本项目的具体目标是：1.发展 基于光谱和内窥镜图像的计算机辅助RTH人工智能模型的实现 多站点部署的系统增强和工具设计；3.使用以下工具执行多站点临床研究 基于ESS和内窥镜图像CAD相结合的人工智能RTH。 方法：首先，我们将在退伍军人事务部波士顿分校进行一项临床研究，收集ESS 结肠镜检查息肉的测量和内窥镜图像。我们将使用此配对数据，关联 根据临床特征和组织病理学，设计和验证计算机辅助RTH的人工智能算法 利用两种光学信息来源的大肠息肉(包括锯齿状病变)。同时， 我们将基于一种极具戏剧性的新设计来构建下一代ESS系统的原型和构建 减少硬件占用空间和成本。我们还将设计和制作可再处理的ESS探头 整合到标准的息肉切除术陷阱中。最后，我们将进行一项多点临床研究，包括 将部署上述工作的另外三个退伍军人管理局设施。的主要终结点 目的是分别评估内窥镜图像的ESS和CAD的性能 朝着Pivi阈值的组合。作为次要终点，我们将利用临床研究来评估和 改善我们的临床系统。