  Visual Inspection with Acetic Acid (VIA) remains the most feasible cervical
cancer screening test in resource-constrained settings of low- and
middle-income countries (LMICs), which are often performed screening camps or
primary/community health centers by nurses instead of the preferred but
unavailable expert Gynecologist. To address the highly subjective nature of the
test, various handheld devices integrating cameras or smartphones have been
recently explored to capture cervical images during VIA and aid decision-making
via telemedicine or AI models. Most studies proposing AI models retrospectively
use a relatively small number of already collected images from specific
devices, digital cameras, or smartphones; the challenges and protocol for
quality image acquisition during VIA in resource-constrained camp settings,
challenges in getting gold standard, data imbalance, etc. are often overlooked.
We present a novel approach and describe the end-to-end design process to build
a robust smartphone-based AI-assisted system that does not require buying a
separate integrated device: the proposed protocol for quality image acquisition
in resource-constrained settings, dataset collected from 1,430 women during VIA
performed by nurses in screening camps, preprocessing pipeline, and training
and evaluation of a deep-learning-based classification model aimed to identify
(pre)cancerous lesions. Our work shows that the readily available smartphones
and a suitable protocol can capture the cervix images with the required details
for the VIA test well; the deep-learning-based classification model provides
promising results to assist nurses in VIA screening; and provides a direction
for large-scale data collection and validation in resource-constrained
settings.
