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Deep learning may improve cervical cancer screening

A study in the Journal of the National Cancer Institute found that a whole-slide imaging platform with a deep-learning p16/Ki-67 dual-stained slide classifier yielded similar sensitivity and significantly higher specificity in human papillomavirus-based cervical cancer screening among women, compared with traditional Pap and manual DS, and led to fewer colposcopy referrals, compared with Pap. The findings suggest that automated DS evaluation "could increase the efficiency of cervical cancer screening by finding more precancers and reducing false positives, which has the potential to eliminate a substantial number of unnecessary procedures among HPV-positive women," researcher Dr. Nicolas Wentzensen said.

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