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: 10417015
• 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/10417015
• 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; 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; artificial intelligence algorithm; 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，验证和部署人造 基于智能（AI）的低成本平台，以使结直肠癌的内窥镜预防（CRC）更多 高效的。我们寻求利用光谱活检工具和自动内窥镜成像来利用我们的工作 解释以创建一个基于 相结合的光学方式和机器学习。目前，结肠镜检查CRC预防取决于 所有息肉的完全去除和组织病理学评估。这种做法导致去除 大量具有可忽略的恶性潜力的息肉。因此，有一个广泛认可的 需要简单，快速和低成本的方法实时“智能”息肉评估以减少活检 成本和风险。为此，由美国胃肠道学会领导的主要专业社会 内窥镜检查（ASGE）已认可纯粹的息肉的光学管理，并提出了 事件采用的指南和可接受的性能阈值（即PIVI语句）。这 过去的十年中，生物光谱技术爆炸了诊断和治疗结直肠的爆炸 肿瘤更精确。尽管已经满足了小小的结肠息肉的几个PIVI阈值，但 由于操作员的技能障碍和 经验。机器学习/人工智能的最新进展及其应用于内窥镜检查 成像，已经显示出自动化第rth以克服操作因子因素的希望。这样的能力将是 最终打开了宽度采用节省成本的大门 小息肉的范例。在这方面，我们将使用弹性散射以工作为基础 光谱学（ESS）活检工具，对RTH表现出了巨大的希望，将其与计算机结合 内窥镜图像的辅助诊断（CAD）。我们假设这些新颖的组合 完全基于AI的技术将导致高度准确，最小的且广泛的技术 可部署的结直肠息肉的方法。目前项目的具体目标是：1。 基于光谱和内窥镜图像的计算机辅助RTH的AI模型； 2。实施 用于多站点部署的系统增强和工具设计； 3。使用 基于AI的RTH基于ESS和内窥镜图像的CAD的组合。 方法论：首先，我们将在波士顿弗吉尼亚州进行临床研究，我们将收集ESS 结肠镜检查时息肉的测量和内窥镜图像。我们将使用此配对数据，相关 临床特征和组织病理学设计和验证计算机辅助的AI算法 使用两个光学信息来源的结直肠息肉（包括序列病变）。同时 我们将基于一个急剧的新设计原型和建立下一代ESS系统 降低了硬件足迹和成本。我们还将设计和原型可重新处理的ESS问题 整合到标准的多型切除术污渍中。最后，我们将进行一项涉及的多站点临床研究 上述工作将部署的其他三个VA设施。主要终点 目的是分别评估ESS和CAD的性能 对PIVI阈值的组合。作为次要终点，我们将使用临床研究评估和 改善我们的临床系统。