Background Human papillomavirus vaccination and cervical screening are lacking in most lower resource settings, where approximately 80% of more than 500 000 cancer cases occur annually. Visual inspection of the cervix following acetic acid application is practical but not reproducible or accurate. The objective of this study was to develop a “deep learning”-based visual evaluation algorithm that automatically recognizes cervical precancer/cancer. Methods A population-based longitudinal cohort of 9406 women ages 18–94 years in Guanacaste, Costa Rica was followed for 7 years (1993–2000), incorporating multiple cervical screening methods and histopathologic confirmation of precancers. Tumor registry linkage identified cancers up to 18 years. Archived, digitized cervical images from screening, taken with a fixed-focus camera (“cervicography”), were used for training/validation of the deep learning-based algorithm. The resultant image prediction score (0–1) could be categorized to balance sensitivity and specificity for detection of precancer/cancer. All statistical tests were two-sided. Results Automated visual evaluation of enrollment cervigrams identified cumulative precancer/cancer cases with greater accuracy (area under the curve [AUC] = 0.91, 95% confidence interval [CI] = 0.89 to 0.93) than original cervigram interpretation (AUC = 0.69, 95% CI = 0.63 to 0.74; P < .001) or conventional cytology (AUC = 0.71, 95% CI = 0.65 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.

The scattering characteristics of the malaria byproduct hemozoin, including its scattering distribution and depolarization, are modeled using Discrete Dipole Approximation (DDA) and compared to those of healthy red blood cells. Scattering (or dark-field) spectroscopy and imaging are used to identify hemozoin in fresh rodent blood samples. A new detection method is proposed and demonstrated using dark-field in conjunction with cross-polarization imaging and spectroscopy. SNRs greater than 50:1 are achieved for hemozoin in fresh blood without the addition of stains or reagents. The potential of such a detection system is discussed.

(Abstracted from J Natl Cancer Inst 2019; doi: 10.1093/jnci/djy225) Ninety-nine percent of cervical cancers cases are caused by infections with a human papillomavirus (HPV). This cancer can be prevented in most women by cervical cancer screening and HPV vaccination.