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

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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.