Improving AI/ML-Readiness of data generated from NIH-funded research on oral cancer screening

提高 NIH 资助的口腔癌筛查研究生成的数据的 AI/ML 就绪性

• 批准号: 10594120
• 负责人: Rongguang Liang
• 金额: $ 28.23万
• 依托单位: UNIVERSITY OF ARIZONA
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-08-10 至 2024-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10594120
• 关键词: Accounting; Administrative Supplement; Algorithms; Arizona; Cancer Detection; Clinical; Data; Data Set; Development; Diagnosis; Early Diagnosis; Equilibrium; FAIR principles; Funding; Image; Label; Lesion; Light; Malignant - descriptor; Malignant Neoplasms; Modeling; Modernization; Morbidity - disease rate; Oncology; Ontology; Optical Coherence Tomography; Oral; Oral Diagnosis; Oropharyngeal Squamous Cell Carcinoma; Parents; Patients; Readiness; Research; Resource-limited setting; Screening for Oral Cancer; Survival Rate; Techniques; Technology; Translating; Translations; Uncertainty; United States National Institutes of Health; Universities; Vision; base; cancer classification; cancer diagnosis; cancer imaging; commercialization; cost; data ecosystem; data interoperability; data repository; deep learning; flexibility; high risk population; image processing; imaging modality; imaging system; improved; low and middle-income countries; machine learning model; malignant mouth neoplasm; mortality; multimodal data; multimodality; open data; optical imaging; oral dysplasia; portability; prototype; tool; trustworthiness

Oral and oropharyngeal squamous cell carcinoma (OSCC) together rank as the sixth most common cancer worldwide, accounting for 400,000 new cancer cases each year. Two-thirds of these cancers occur in low- and middle-income countries (LMICs). While the 5-year survival rate in the U.S. is 62%, the survival rate is only 10- 40% and cure rate around 30% in the developing world. To meet the need for technologies that enable comprehensive oral cancer screening and diagnosis in low resource settings (LRS). In the parent R01DE030682 project titled “Multimodal Intraoral Imaging System for Oral Cancer Detection and Diagnosis in Low Resource Setting”, we have formed an interdisciplinary team with complementary expertise in optical imaging, oncology, deep learning, technology translation, and commercialization to develop, validate, and clinically translate a multimodal intraoral imaging system for oral cancer detection and diagnosis. We will achieve the project objective through three Aims: (1) develop a portable, semi-flexible, and compact multimodal intraoral imaging system; (2) evaluate the clinical feasibility of the prototyped intraoral imaging system and develop deep learning based image processing algorithms for early detection, diagnosis, and mapping of oral dysplastic and malignant lesions; and (3) validate the capability of the prototyped intraoral imaging system for diagnosing oral dysplasia and malignant lesions. In our UH3CA239682 project titled “Low-cost Mobile Oral Cancer Screening for Low Resource Setting”, we have screened ~7,000 high-risk population for oral cancer and obtained at least two pairs of dual-modal images (white light and autofluorescence) from each patient and obtained more than 28,000 de-identified images and related information. It is the largest image dataset on oral cancers. With this Administrative Supplements, we will make the image data AI/ML-ready by improving data compatibility with AI/ML tools, cleaning dataset, balancing data, reducing uncertainty, improving the interoperability of the data with ontology, and improving trustworthiness of AI/ML models using pixel-level annotation. We will also demonstrate the use of the transformed data in AI/ML applications through (1) multi-class oral cancer classification using the transformed multi-modal data and (2) interpretable and trustworthy AI model using image-level labels and pixel-level annotation. The image data and machine learning models will be available through The University of Arizona Research Data Repository (ReDATA). Completion of this project will accelerate development of AI/ML-based techniques for early oral cancer detection in low-resource settings, reducing morbidity and mortality. It will make data FAIR (Findable, Accessible, Interoperable, and Reusable) with high impact for open science, contributing to the NIH vision of a modernized and integrated biomedical data ecosystem. The parent R01 project will directly benefit from this dataset and the developed AI/ML algorithms as deep learning segmentation based on dual-modal images will be used to locate the suspicious regions for optical coherence tomography (OCT) imaging.

口腔和口咽鳞状细胞癌(OSCC)在最常见的癌症中排名第六 全球每年新增癌症病例40万例。这些癌症中有三分之二发生在低位和 中等收入国家(LMIC)。虽然美国的5年存活率为62%，但存活率只有10%- 发展中国家为40%，治愈率约为30%。满足对支持以下功能的技术的需求 在低资源环境(LRS)中进行全面的口腔癌筛查和诊断。在父R01DE030682中 低资源口腔癌检测与诊断多模式口腔内成像系统 背景“，我们已经组成了一个跨学科的团队，在光学成像、肿瘤学、 深度学习、技术翻译和商业化，以开发、验证和临床翻译 用于口腔癌检测和诊断的多模式口腔内成像系统。我们将实现项目目标 通过三个目标：(1)开发便携式、半灵活和紧凑的多模式口腔内成像系统；(2) 评估口腔内成像原型系统的临床可行性并开发基于深度学习的图像 用于口腔发育不良和恶性病变的早期检测、诊断和绘图的处理算法；以及 (3)验证样机口腔内成像系统对口腔异型增生和恶性病变的诊断能力。 损伤。 在我们的UH3CA239682项目中，我们为低资源环境提供了低成本的移动口腔癌筛查服务 已对约7000名口腔癌高危人群进行了筛查，并获得了至少两对双模图像 (白光和自发荧光)，并获得了超过28,000张未识别的图像和 相关信息。这是关于口腔癌的最大图像数据集。有了本行政副刊，我们将 通过提高与AI/ML工具的数据兼容性、清理数据集、平衡 数据，减少不确定性，提高数据与本体的互操作性，提高可信性 使用像素级注释的AI/ML模型。我们还将演示如何在AI/ML中使用转换后的数据 通过(1)使用转换后的多模式数据的多类口腔癌分类和(2)应用 使用图像级标签和像素级注释的可解释和可信的AI模型。 图像数据和机器学习模型将通过亚利桑那大学研究中心提供 数据存储库(ReDATA)。该项目的完成将加速基于AI/ML的技术的发展 用于低资源环境下的口腔癌早期检测，降低发病率和死亡率。它将使数据变得公平 (可查找、可访问、可互操作和可重复使用)对开放科学产生了巨大影响，为NIH做出了贡献 一个现代化和集成的生物医学数据生态系统的愿景。母公司R01项目将直接受益 从该数据集和所开发的AI/ML算法作为基于双模式的深度学习分割 图像将被用来定位光学相干层析(OCT)成像的可疑区域。