A rapid quantitative real-time PCR method was employed to quantify the copy number of E2 and E6 genes for analysis of the physical status of human papillomavirus type 16 (HPV-16) DNA. Significant differences with respect to both copy numbers were found when more than 40% of HPV-16 DNA was integrated with disruption of the E2 gene in an experimental model. The physical status of HPV-16 DNA in 50 clinical samples was exclusively episomal in 21 cases (42%), concomitant in 14 cases (28%), and integrated in 15 cases (30%). The prevalence of integrated and/or concomitant forms of HPV-16 DNA increased with progression of cervical disease. Four of 11 cervical intraepithelial neoplasia involved integrated forms of HPV-16 DNA partially or exclusively. This rapid, sensitive technique is useful in the analysis of the physical status of HPV DNA.

Purpose: To make the artificial intelligence (AI) classifiers of the image of the blastocyst implanted later in order to predict the probability of achieving live birth.Methods: A system for using the machine learning approaches, which are logistic regression, naive Bayes, nearest neighbors, random forest, neural network, and support vector machine, of artificial intelligence to predict the probability of live birth from a blastocyst image was developed. Eighty images of blastocysts that led to live births and 80 images of blastocysts that led to aneuploid miscarriages were used to create an AI‐based method with 5‐fold cross‐validation retrospectively for classifying embryos.Results: The logistic regression method showed the best results. The accuracy, sensitivity, specificity, positive predictive value, and negative predictive value were 0.65, 0.60, 0.70, 0.67, and 0.64, respectively. Area under the curve was 0.65 ± 0.04 (mean ± SE). Estimated probability of belonging to the live birth category was found significantly related to the probability of live birth (P < 0.005).Conclusions: Classifiers using artificial intelligence applied toward a blastocyst image have a potential to show the probability of live birth being the outcome.

Purpose: To identify artificial intelligence (AI) classifiers in images of blastocysts to predict the probability of achieving a live birth in patients classified by age. Results are compared to those obtained by conventional embryo (CE) evaluation.Methods: A total of 5691 blastocysts were retrospectively enrolled. Images captured 115 hours after insemination (or 139 hours if not yet large enough) were classified according to maternal age as follows: <35, 35‐37, 38‐39, 40‐41, and ≥42 years. The classifiers for each category and a classifier for all ages were related to convolutional neural networks associated with deep learning. Then, the live birth functions predicted by the AI and the multivariate logistic model functions predicted by CE were tested. The feasibility of the AI was investigated.Results: The accuracies of AI/CE for predicting live birth were 0.64/0.61, 0.71/0.70, 0.78/0.77, 0.81/0.83, 0.88/0.94, and 0.72/0.74 for the age categories <35, 35‐37, 38‐39, 40‐41, and ≥42 years and all ages, respectively. The sum value of the sensitivity and specificity revealed that AI performed better than CE (P = 0.01).Conclusions: AI classifiers categorized by age can predict the probability of live birth from an image of the blastocyst and produced better results than were achieved using CE.