AI-Assisted Cervical Cancer Screening
<|reference_start|>AI-Assisted Cervical Cancer Screening: 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.<|reference_end|>
@article{poudel2024ai-assisted,
    title={AI-Assisted Cervical Cancer Screening},
    author={Kanchan Poudel, Lisasha Poudel, Prabin Raj Shakya, Atit Poudel,
  Archana Shrestha, Bishesh Khanal},
    journal={arXiv preprint arXiv:2403.11936},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.11936},
    primaryClass={eess.IV cs.CV}
}