文件名称：Deep learning for cervical cancer screening
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更新时间：2022-05-02 19:58:25
deep l
Automated visual evaluation of enrollment cervigrams identified cumulative precancer/cancer cases with greater accuracy (area under the curve [AUC]¼0.91, 95% confidence interval [CI]¼0.89 to 0.93) than original cervigraminterpretation (AUC¼0.69, 95% CI¼0.63 to 0.74; P<.001) or conventional cytology (AUC¼0.71, 95% CI¼0.65 to 0.77; P<.001). A single visual screening round restricted to women at the prime screening ages of 25–49 years could identify 127 (55.7%) of 228 precancers (cervical intraepithelial neoplasia 2/cervical intraepithelial neoplasia 3/adenocarcinoma in situ [AIS]) diagnosed cumulatively in the entire adult population (ages 18–94 years) while referring 11.0% for management. Conclusions: The results support consideration of automated visual evaluation of cervical images from contemporary digital cameras. If achieved, this might permit dissemination of effective point-of-care cervical screening.