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.

口服和口咽鳞状细胞癌（OSCC）列为第六个最常见的癌症 全球，每年占40万例新癌症病例。这些癌症中有三分之二发生在低 - 和 中等收入国家（LMIC）。虽然美国的5年生存率为62％，但生存率仅为10- 发展中国家的40％和治愈率约为30％。满足对启用技术的需求 低资源环境（LRS）中的全面口腔癌筛查和诊断。在父r01de030682中 项目标题为“用于低资源中口腔癌检测和诊断的多模式内成像系统 设置”，我们组成了一个跨学科团队，具有光学成像，肿瘤学方面的完整专业知识， 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内成像系统。我们将实现项目目标 通过三个目的：（1）开发一种便携式，半柔性和紧凑的多模式内成像系统； （2） 评估原型口内成像系统的临床可行性并发展基于深度学习的图像 处理口服异型和恶性病变的早期检测，诊断和映射的处理算法；和 （3）验证原型的口腔内成像系统的能力，用于诊断口腔发育不良和恶性肿瘤 病变。 在我们的UH3CA239682项目中，名为“低成本移动口腔癌筛查低资源设置”，我们 已经筛选了约7,000个口腔癌的高风险人群，并至少获得了两对双模式图像 （白光和自动荧光）来自每位患者，并获得了28,000多个被识别的图像和 相关信息。它是口服癌症上最大的图像数据集。使用此管理补充剂，我们将 通过使用AI/ML工具，清洁数据集改善数据兼容性来使图像数据AI/ML准备好 数据，降低不确定性，改善数据与本体论的互操作性并提高可信度 使用像素级注释的AI/ML模型。我们还将证明在AI/ML中使用转换数据的使用 通过（1）使用转换的多模式数据和（2）的多级口腔癌分类应用 使用图像级标签和像素级注释的可解释和值得信赖的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获得 数据存储库（redata）。该项目的完成将加速基于AI/ML的技术的开发 对于低农源环境中的早期口腔癌检测，可降低发病率和死亡率。这将使数据公平 （可发现，可访问，可互操作和可重复使用）对开放科学的影响很大，为NIH做出了贡献 现代化和集成的生物医学数据生态系统的愿景。家长R01项目将直接受益 从该数据集和开发的AI/ML算法作为基于双模式的深度学习细分 图像将用于定位可疑区域以进行光学相干断层扫描（OCT）成像。