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.

摘要