Development and Validation of an Artificial-Intelligence-enabled Portable Colposcopy Device for Optimizing Triage Alternatives for HPV-based Cervical Cancer Screening

开发和验证人工智能便携式阴道镜设备，用于优化基于 HPV 的宫颈癌筛查的分诊方案

• 批准号: 10416639
• 负责人: Elizabeth Anne BUKUSI
• 金额: $ 60.41万
• 依托单位: DUKE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-05-20 至 2027-04-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10416639
• 关键词: Acetic Acids; Address; Adopted; Affect; Algorithms; Ambulatory Care; Artificial Intelligence; Biopsy; Caring; Cause of Death; Cellular Phone; Cervical; Cervical Cancer Screening; Cervix Uteri; Cessation of life; Characteristics; Clinic; Clinical; Clinical Research; Colposcopes; Colposcopy; Computer software; Country; Coupled; Cytology; Data; Databases; Decision Aid; Development; Devices; Diagnosis; Diagnostic; Disease; Educational Status; Effectiveness; Ensure; Evaluation; Generations; Goals; Guidelines; HPV-High Risk; Hand; Health; Health care facility; Healthcare; Hemorrhage; Human Papillomavirus; Human Resources; Image; Incidence; Income; Infertility; Infrastructure; Intervention; Kenya; Knowledge; Letters; Light; Location; Malignant Neoplasms; Malignant neoplasm of cervix uteri; Medical Research; Methods; Modeling; National Cancer Institute; Pathology; Performance; Physicians; Pilot Projects; Population; Predictive Value; Prevention strategy; Process; Provider; Public Health; ROC Curve; Research; Research Institute; Resolution; Resources; Retrieval; Risk; Sensitivity and Specificity; Series; Site; Testing; Training; Triage; Validation; Visit; Visual; Woman; Work; World Health Organization; base; burden of illness; cervical cancer prevention; clinical research site; comparative effectiveness; contrast imaging; convolutional neural network; cost; deep learning; deep learning algorithm; diagnostic accuracy; diagnostic strategy; experience; follow-up; global health; impression; improved; innovation; low and middle-income countries; mHealth; machine learning algorithm; mortality; overtreatment; portability; prospective; prospective test; prototype; real world application; risk prediction model; risk stratification; screening; standard care; success; technology development; tool; virtual

Abstract Cervical cancer is the second leading cause of death for women worldwide. Alarmingly, 85% of deaths occur in low and middle-income countries (LMICs), as they lack the health care infrastructure required for cytology-based screening, referral colposcopy diagnosis, and expert physicians, which have dramatically reduced the disease burden in high income countries (HICs). Highly sensitive human papillomavirus (HPV) testing has been effective at reducing the incidence and mortality from cervical cancer when directly coupled with treatment; however, a majority of women with HPV do not have cervical precancer, making HPV testing a poor triage test as overtreatment carries risks like hemorrhage and infertility. Colposcopy followed by biopsy, the preferred triage method in HICs, is untenable in most LMIC settings due to the cost of colposcopes and pathology facilities to process and interpret biopsy results. To make matters worse, women are lost to follow up in LMIC settings when a multi-visit model for cervical cancer screening is used. Visual Inspection with Acetic Acid (VIA), the World Health Organization recommended triage test following HPV testing, has widely varied sensitivity and specificity depending on the training level of the provider. In this proposal we are proposing a single visit model for precision diagnosis and treatment in LMICs for cervical cancer prevention. Two major technological tools are needed to implement this model: a low-cost method to perform imaging of the cervix and a machine learning algorithm to automate diagnosis in the absence of a provider. We have previously developed the Pocket Colposcope, which has shown high concordance with standard colposcopy at a fraction of the cost and validated it on thousands of women across nearly every continent. We are now in the process of developing a state-of-the-art convolutional neural network (CNN), called Colposcopy Automated Risk Evaluation (CARE), trained with Pocket colposcopy images to automate the diagnostic process. Our current prototype algorithm has been highly successful at classifying cervical pre-cancers from Pocket Colposcope images retrospectively. Our goals for this proposal are fourfold: 1) improve and generalize the performance of Pocket CARE using >10,000 National Cancer Institute (NCI) standard colposcopy images; 2) generate synthetic images to address domain shifts due to environmental and personnel changes between different clinical sites; 3) embed the CARE algorithm into our existing software to enable high quality image capture with the Pocket Colposcope for automated diagnosis 4) validate the performance of Pocket CARE prospectively with a clinical study in Kisumu, Kenya, a site where Pocket CARE would ultimately be adopted\. The deliverables for this proposal will be a fully validated Pocket CARE software ready for scale to different clinical scenarios based on location-specific cultural contexts and infrastructure and a comparative effectiveness of Pocket CARE to other publicly available algorithms and standard RI care.

抽象的 宫颈癌是全球女性死亡的第二大死亡原因。令人震惊的是，85％的死亡发生在 低收入和中等收入国家（LMIC），因为它们缺乏基于细胞学的医疗保健基础设施 筛查，转诊阴道镜诊断和专家医师，这些医师大大降低了该疾病 高收入国家（HIC）的负担。高度敏感的人乳头瘤病毒（HPV）测试已经有效 直接与治疗直接结合时，降低宫颈癌的发病率和死亡率；但是， 大多数HPV女性没有宫颈预科师 过度处理的风险像 出血和不育。 阴道镜进行活检，首选分类 HIC中的方法在大多数LMIC设置中都站不住脚，因为阴道镜和病理设施的成本 过程和解释活检结果。更糟糕的是，妇女在LMIC环境中失去了跟进 使用用于宫颈癌筛查的多访问模型。用乙酸（VIA）视觉检查，世界 健康组织建议在HPV测试后进行分类测试，其灵敏度和特异性各不相同 取决于提供者的培训水平。在此提案中，我们提出了一个单一的访问模型以确保精确 LMIC的诊断和治疗预防宫颈癌。需要两个主要的技术工具来 实现此模型：一种低成本方法，用于执行子宫颈成像和机器学习算法的成像 在没有提供商的情况下自动诊断。我们以前已经开发了口袋阴道镜， 与标准阴道镜的一小部分表现出很高的一致性，并在数千个成本上进行了验证 几乎每个大陆的妇女。我们现在正在开发最先进的卷积 神经网络（CNN），称为阴道镜自动化风险评估（CARE），接受口袋阴道镜训练 图像以自动化诊断过程。我们当前的原型算法在 回顾性的口袋阴道镜图像对宫颈前癌进行分类。我们为此提出的目标是 四倍：1）使用> 10,000个国家癌症研究所改善和推广口袋护理的表现 （NCI）标准阴道镜图像； 2）生成合成图像以解决由于环境而引起的域移动 以及人员之间的人员变化在不同的临床部位之间； 3）将护理算法嵌入到我们现有的软件中 用口袋阴道镜实现自动诊断的高质量图像捕获4）验证 在肯尼亚基苏木（Kisumu 最终将被采用\。该提案的可交付成果将是一个经过全面验证的口袋护理软件 准备根据特定于位置的文化环境和基础设施来了解不同的临床场景 口袋护理与其他公共可用算法和标准RI护理的比较有效性。