Point-of-care cervical cancer screening using deep learning-based microholography

Abstract

Most deaths (80%) from cervical cancer occur in regions lacking adequate screening infrastructures or ready access to them. In contrast, most developed countries now embrace human papillomavirus (HPV) analyses as standalone screening; this transition threatens to further widen the resource gap.Methods: We describe the development of a DNA-focused digital microholography platform for point-of-care HPV screening, with automated readouts driven by customized deep-learning algorithms. In the presence of high-risk HPV 16 or 18 DNA, microbeads were designed to bind the DNA targets and form microbead dimers. The resulting holographic signature of the microbeads was recorded and analyzed.Results: The HPV DNA assay showed excellent sensitivity (down to a single cell) and specificity (100% concordance) in detecting HPV 16 and 18 DNA from cell lines. Our deep learning approach was 120-folder faster than the traditional reconstruction method and completed the analysis in < 2 min using a single CPU. In a blinded clinical study using patient cervical brushings, we successfully benchmarked our platform's performance to an FDA-approved HPV assay.Conclusions: Reliable and decentralized HPV testing will facilitate cataloguing the high-risk HPV landscape in underserved populations, revealing HPV coverage gaps in existing vaccination strategies and informing future iterations.

Development and interpretation of a pathomics-based model for the prediction of microsatellite instability in Colorectal Cancer

“…The cell level phenotypes presented in WSI are affected by genotypes such as MSI at the molecular scale. With the continuous penetration of artificial intelligence (AI) into the field of medical imaging, researchers have sought solutions based on deep learning, a research area in AI, in a wide range of medical problems, such as prediction of gene mutations 14 and tumor-infiltrating lymphocytes 12 , and cancer screening 15 , 16 . Whereas traditional machine learning depends largely on human-selected features 17 , deep learning can learn features from the data, which makes it possible for researchers to discover untapped information 18 , 19 .…”

A systematic review on deep learning‐based automated cancer diagnosis models

“…developed a DNA‐focused digital micro holography method for HPV screening, with automated methods using deep‐learning algorithms. The result of this paper shows the excellent sensitivity and specificity (100% concordance) in detecting HPV 16 and 18 DNA from cell lines 79 …”

“…Pathania et al developed a DNA-focused digital micro holography method for HPV screening, with automated methods using deep-learning algorithms. The result of this paper shows the excellent sensitivity and specificity (100% concordance) in detecting HPV 16 and 18 DNA from cell lines 79. Sompawong et al presented the method for detecting cervical cancer employing Pap smear histology slides using the Mask Regional Convolution Neural Network (Mask R-CNN).…”

Artificial Intelligence in Cervical Cancer Screening and Diagnosis

“…The system highlighted the patients who are at high risk of developing CIN2/3+, demonstrated that some infections involving multiple HPV types carry additional risks, and identified the most important gene combinations (29). Pathania proposed the HPV AI surveillance, which uses a deep learning (DL) algorithm combined with digital micro-holography, and reported excellent sensitivity and specificity (100% coincidence) in detecting HPV 16 DNA and HPV 18 DNA in cell lines (30). However, more studies are underway.…”