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.

在低收入和中等收入中，口服和口服和口咽鳞状细胞癌（OSCC）出现三分之二 国家（LMIC），5年的生存率仅为10-40。已经知道该疾病及其 预防采取行动。 LMIC的存活率差主要是由于晚期诊断。因此，是 必须尽早并迅速检测前身和恶性病变。 满足对低资源环境中口腔癌筛查和诊断的技术的需求 （LRS），Light Research Inc（LRI）将开发，验证和商业化基于低成本的智能手机成像 为非专家量身定制的远程专家访问和分类决策指导的系统 在LRS中使用。 为了实现项目目标，LRI将获得亚利桑那大学（UA）的技术许可，并与UA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 口腔癌检测和LRS诊断的口服成像系统。在过去的几年中，项目团队有 开发和评估了双模式（偏振白光成像[PWLI]）和自动荧光成像 [AFI]））移动成像设备，该设备专门针对LRS的关键障碍，以改善口腔癌筛查。 为了解决采用光学成像技术的关键障碍，用于LRS中的口腔癌筛查 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 指导患者分类的方法。 由于该移动成像系统中提出的关键技术已成功评估了口头 LRS中有3,000个高危人群的癌症筛查，将其成功转变为低风险 用于准确，客观和位置分辨的口腔癌检测的资源区域非常高。项目 将通过三个目标实现目标：（1）优化用于LRS的移动内部成像系统，（2） 优化基于深度学习的双模式图像分类方法，（3）验证临床 在LSR中，移动口服成像系统对口腔癌筛查和分类的有用性。 该项目的成功完成将导致具有深度学习图像的移动口服成像系统 分类方法向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）低成本生产，以及Ergo Healthcare（印度孟买）的产品 在南亚和东南亚的分布，以及Dentalez集团在美国的产品分销和 欧洲。使用这种基于移动的筛查方法进行早期检测和口服癌症分类 最终改善了LRS患者的口腔癌检测率，治疗结果和患者的生活质量。