Mobile oral cancer screening system for low-resource settings

适用于资源匮乏环境的移动口腔癌筛查系统

• 批准号: 10323692
• 负责人: PETRA E WILDER-SMITH
• 金额: $ 95.29万
• 依托单位: LIGHT RESEARCH, INC.
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-09-13 至 2023-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10323692
• 关键词: Address; Adopted; Americas; Arizona; Biopsy; Cancer Center; Cancer Detection; Cancer Patient; Caring; Cellular Phone; Classification; Clinical; Data; Data Set; Decision Making; Detection; Devices; Diagnosis; Diagnostic; Disease; Disease Progression; Dysplasia; Early Diagnosis; Electronics; Europe; Goals; Health; Healthcare; Histopathology; Image; Imaging Device; Imaging Techniques; India; Infrastructure; Internet; Learning; Lesion; Licensing; Light; Lighting; Location; Malignant - descriptor; Malignant Neoplasms; Mechanics; Methods; Modality; Morbidity - disease rate; Oral; Oral cavity; Oropharyngeal Squamous Cell Carcinoma; Participant; Patient Triage; Patients; Performance; Phase; Prevention; Process; Production; Quality of life; Research; Resources; Schedule; Screening for Oral Cancer; Solid; South Asian; Specialist; Survival Rate; System; Techniques; Technology; Telephone; Time; Treatment outcome; Triage; Universities; base; cancer diagnosis; cancer prevention; classification algorithm; clinical practice; cloud based; community setting; cost; deep learning; deep learning algorithm; design; flexibility; handheld mobile device; high resolution imaging; high risk population; imaging modality; imaging probe; imaging system; impression; improved; innovation; intraoral probe; low and middle-income countries; malignant mouth neoplasm; mobile application; mortality; multimodality; neural network architecture; optical imaging; oral care; oral lesion; portability; prototype; recruit; remote diagnosis; rural area; screening; social; southeast Asian; standard of care; tongue root

Two-thirds of oral and oropharyngeal squamous cell carcinoma (OSCC) occur in low- and middle-income countries (LMICs), the 5-year survival rate is only 10-40. Enough is already known about the disease and its prevention for action to be taken. The poor survival rate in LMICs is mainly due to late diagnosis. Therefore, it is imperative to detect precursor and malignant lesions early and expeditiously. To meet the need for technologies that enable oral cancer screening and diagnosis in low resource settings (LRS), Light Research Inc (LRI) will develop, validate, and commercialize a low-cost smartphone-based imaging system that provides remote specialist access and triage decision-making guidance tailored to non-specialist use in LRS. To achieve the project goal, LRI will license technologies from University of Arizona (UA), and partner with UA and Mazumdar-Shaw Cancer Center (MSCC) (Bangalore, India) to develop and validate a multimodal mobile oral imaging system for oral cancer detection and diagnosis in LRS. In the past few years, the project team has developed and evaluated a dual-mode (polarized white light imaging [pWLI]) and autofluorescence imaging [AFI])) mobile imaging device that specifically addresses critical barriers in LRS to improve oral cancer screening. To address one of the key hurdles in adopting optical imaging techniques for oral cancer screening in LRS, the team has also developed and evaluated cloud-based and mobile-based deep learning image classification methods for guiding patient triage. Since the key techniques proposed in this mobile imaging system have been successfully evaluated for oral cancer screening with 3,000 high-risk population in LRS, the potential of successfully transitioning it to the low- resource regions for accurate, objective and location-resolved detection of oral cancer is very high. The project objective will be achieved through three Aims: (1) to optimize a mobile intraoral imaging system for LRS, (2) to optimize deep learning-based dual-modality image classification methods, and (3) to validate the clinical usefulness of the mobile oral imaging system for oral cancer screening and triage in LSR. Successful completion of this project will lead to a mobile oral imaging system with deep learning image classification method that delivers urgently-needed capabilities to the end users in LRS. LRI will partner with Jana Care Inc (New Delhi, India) for low-cost production, with Ergo Healthcare (Mumbai, India) for product distribution in south and southeast Asian, and with DentalEZ Group for product distribution in America and Europe. The use of this mobile-based screening approach for early detection and triage of oral cancers will eventually improve oral cancer detection rates, treatment outcomes, and quality of life of patients in LRS.

三分之二的口腔和口咽鳞状细胞癌(OSCC)发生在低收入和中等收入人群 在低收入国家(LMIC)，5年存活率只有10-40。人们已经足够了解这种疾病和它的 预防需要采取的行动。LMICs存活率低的主要原因是诊断较晚。因此，它是 必须及早和迅速发现前驱病变和恶性病变。 满足对能够在低资源环境下进行口腔癌筛查和诊断的技术的需求 (LRS)，Light Research Inc.(LRI)将开发、验证基于智能手机的低成本成像并将其商业化 提供远程专家访问和为非专家量身定制的分类决策指导的系统 在LRS中使用。 为了实现项目目标，LRI将从亚利桑那大学(UA)获得技术许可，并与亚利桑那大学合作 和Mazumdar-Shaw癌症中心(MSCC)(印度班加罗尔)开发和验证多模式移动 口腔影像系统用于口腔癌的检测和诊断。在过去的几年里，项目组已经 开发并评估了双模(偏振白光成像[pWLI])和自体荧光成像 [AFI])移动成像设备，专门解决LRS中的关键障碍，以改进口腔癌筛查。 为解决在LRS中采用光学成像技术筛查口腔癌的其中一个关键障碍， 该团队还开发和评估了基于云和基于移动的深度学习图像分类 指导病人分诊的方法。 由于该移动成像系统中提出的关键技术已经成功地用于口腔评估 在LRS对3,000名高危人群进行癌症筛查，成功将其过渡到低风险人群的潜力- 准确、客观和位置分辨的口腔癌检测的资源区非常高。该项目 目标将通过三个目标来实现：(1)优化用于LRS的移动式口腔内成像系统，(2) 优化基于深度学习的双通道图像分类方法；(3)临床验证 移动式口腔成像系统在口腔癌筛查和分类中的应用。 该项目的成功完成将带来一种具有深度学习图像的移动口腔成像系统 一种分类方法，为LRS中的最终用户提供急需的功能。LRI将与 Jana Care Inc(印度新德里)提供低成本生产，Ergo Healthcare(印度孟买)提供产品 在南亚和东南亚分销，并与DentalEz Group合作在美国和 欧洲。使用这种基于移动设备的筛查方法对口腔癌进行早期检测和分类将 最终提高LRS患者的口腔癌发现率、治疗结果和生活质量。