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.