The development of “automated visual evaluation” for cervical cancer screening: The promise and challenges in adapting deep‐learning for clinical testing

Abstract

There is limited access to effective cervical cancer screening programs in many resource-limited settings, resulting in continued high cervical cancer burden. Human papillomavirus (HPV) testing is increasingly recognized to be the preferable primary screening approach if affordable due to superior long-term reassurance when negative and adaptability to self-sampling. Visual inspection with acetic acid (VIA) is an inexpensive but subjective and inaccurate method widely used in resource-limited settings, either for primary screening or for triage of HPV-positive individuals. A deep learning (DL)-based automated visual evaluation (AVE) of cervical images has been developed to help improve the accuracy and reproducibility of VIA as assistive technology. However, like any new clinical technology, rigorous evaluation and proof of clinical effectiveness are required before AVE is implemented widely. In the current article, we outline essential clinical and technical considerations involved in building a validated DL-based AVE tool for broad use as a clinical test.

False Negative Results in Cervical Cancer Screening—Risks, Reasons and Implications for Clinical Practice and Public Health

“…The need for the appropriate and regular training of health workers providing VIA is, therefore, essential. Additionally, the use of artificial intelligence is currently discussed as a helpful tool for distinguishing clinically significant lesions from others [ 125 ].…”

“…The results seemed promising [ 194 ]. However, further research is needed for its proper validation before its implementation into real-life screening [ 125 ]. Additionally, appropriate nomenclature and colposcopic protocols were developed as a response to the need for the unification of standards [ 127 , 128 ].…”

Assessment of the implementation context in preparation for a clinical study of machine-learning algorithms to automate the classification of digital cervical images for cervical cancer screening in resource-constrained settings

“…Screen-and-treat (SAT) is a strategy for cervical cancer screening that has been shown to improve outcomes and is currently recommended by the World Health Organization for low-and middle-income country (LMIC) settings (2). Recently developed smartphone technologies that utilize machine-learning algorithms to automate the classification of digital cervical images are an encouraging method with the SAT approach to address the achievement gap in LMICs (3)(4)(5)(6)(7). Preliminary studies suggest that Automated Visual Evaluation (AVE) devices could detect cervical cancer precursor lesions in some populations (3,5,7).…”

“…Recently developed smartphone technologies that utilize machine-learning algorithms to automate the classification of digital cervical images are an encouraging method with the SAT approach to address the achievement gap in LMICs (3)(4)(5)(6)(7). Preliminary studies suggest that Automated Visual Evaluation (AVE) devices could detect cervical cancer precursor lesions in some populations (3,5,7). Private companies have developed proprietary devices to obtain images and run automated classifiers to improve screening (8,9).…”

“…Other groups are using non-proprietary cell phones to take cervical photographs to integrate automated classifiers through apps or other interfaces eventually. Requirements for successful use of AVE devices highlight attributes of the device and the algorithm that affect cervical image quality, but not the clinical setting (3). Pragmatic trialists advocate for measuring these operational attributes that influence study outcomes and ultimately impact the translation of scientific innovation to practice settings, but this is rarely done (10-12).…”