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）一起列为第六大常见癌症 全球每年新增40万例癌症病例。这些癌症中有三分之二发生在低- 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- 发展中国家的治愈率约为30%。为了满足对技术的需求， 在低资源环境中进行全面的口腔癌筛查和诊断。在父R 01 DE 030682中 项目名为“用于低资源口腔癌检测和诊断的多模式口内成像系统 我们成立了一个跨学科的团队，在光学成像，肿瘤学， 深度学习、技术转化和商业化，以开发、验证和临床转化 用于口腔癌检测和诊断的多模式口内成像系统。我们会达到计划的目标 通过三个目标：（1）开发一种便携式、半柔性和紧凑的多模式口内成像系统;（2） 评估原型口内成像系统的临床可行性，并开发基于深度学习的图像 用于口腔发育异常和恶性病变的早期检测、诊断和映射的处理算法;以及 (3)验证原型口内成像系统诊断口腔发育不良和恶性肿瘤的能力 病变 在我们的UH 3CA 239682项目“低资源环境下的低成本移动的口腔癌筛查”中， 已经筛查了约7，000名口腔癌高危人群，并获得了至少两对双模态图像 （白色光和自体荧光），并获得了超过28，000个去识别图像， 相关信息。它是关于口腔癌的最大图像数据集。有了这些行政补充，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，并提高可信度 使用像素级注释的AI/ML模型。我们还将演示在AI/ML中使用转换后的数据 通过（1）使用变换的多模态数据的多类口腔癌分类和（2） 使用图像级标签和像素级注释的可解释和可信赖的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 在低资源环境中进行早期口腔癌检测，降低发病率和死亡率。它将使数据公平 （可查找、可解释、可互操作和可重用），对开放科学具有很大影响，为NIH做出贡献 一个现代化和集成的生物医学数据生态系统的愿景。R 01母项目将直接受益 从这个数据集和开发的AI/ML算法作为基于双模态的深度学习分割， 图像将用于定位光学相干断层扫描（OCT）成像的可疑区域。