Feasibility of predicting live birth by combining conventional embryo evaluation with artificial intelligence applied to a blastocyst image in patients classified by age

Abstract

Purpose To identify the multivariate logistic regression in a combination (combination method) involving artificial intelligence (AI) classifiers in images of blastocysts along with a conventional embryo evaluation (CEE) to predict the probability of accomplishing a live birth in patients classified by maternal age. Methods Retrospectively, a total of 5691 blastocysts were enrolled. Images captured 115 hours or 139 hours if not yet sufficiently large after insemination were classified according to age as follows: <35, 35‐37, 38‐39, 40‐41, and ≥42 years old. The classifiers for each category were created by using convolutional neural networks associated with deep learning. Next, the feasibility of a method combining AI with multivariate logistic model functions by CEE was investigated. Results The values of the area under the curve (AUC) and the accuracies to predict live birth achieved by the CEE/AI/combination methods were 0.651/0.634/0.655, 0.697/0.688/0.723, 0.771/0.728/0.791, 0.788/0.743/0.806 and 0.820/0.837/0.888, and 0.631/0.647/0.616, 0.687/0.675/0.671, 0.725/0.697/0.732, 0.714/0.776/0.801, and 0.910/0.866/0.784 for age categories of <35, 35‐37, 38‐39, 40‐41, and ≥42 years old, respectively. Conclusions Though there were mostly no significant differences regarding the AUC and the sensitivity plus specificity in all age categories, the combination method seemed to be the best.

Application of deep learning to the classification of uterine cervical squamous epithelial lesion from colposcopy images combined with HPV types

“…In medicine, several studies have used AI for deep learning with convolutional neural networks (48,49). The accuracy values of AI with deep learning have been published and include 0.997 for the histopathological diagnosis of breast cancer (50), 0.980 for the morphological quality of blastocysts and evaluation by an embryologist (51), 0.640-0.880 for predicting live birth from a blastocyst image of patients by age (4,52), 0.650 for predicting live birth without aneuploidy from a blastocyst image (53), 0.823 (3), 0.720 (54) and 0.500 (55) for colposcopy, 0.830 to 0.900 for the early diagnosis of Alzheimer's disease (56), 0.830 for urological dysfunctions (57) and 0.830 for the diagnostic imaging of orthopedic trauma (58). A number of studies have reported a limitation of conventional colposcopy.…”

“…For example, it has been reported that using AI-assisted colposcopy may reduce the time and effort it takes for a gynecologist to become a colposcopy expert, resulting in more time to improve other skills, training and activities (3). Moreover, the use of AI for predicting live births from blastocysts, to a level similar to that of specialists, may result in time saved for embryologists, reducing the financial costs of training (4). The aim of the present study was to investigate the feasibility of applying deep learning, a type of AI using both image and non-image information simultaneously, for gynecological clinical practice.…”

Application of deep learning to the classification of uterine cervical squamous epithelial lesion from colposcopy images

“…Several reports have used AI (55) for deep learning with convolutional neural networks in medicine (56). The accuracy values of this method with deep learning have been published and include 0.997 for the histopathological diagnosis of breast cancer (57), 0.90–0.83 for the early diagnosis of Alzheimer's disease (58), 0.83 for urological dysfunctions (59), 0.72 (60) and 0.50 (61) for colposcopy, 0.68–0.70 for localization of rectal cancer (62), 0.83 for the diagnostic imaging of orthopedic trauma (63), 0.98 for the morphological quality of blastocysts and evaluation by an embryologist (64), 0.65 for predicting live birth without aneuploidy from a blastocyst image (65) and 0.64–0.88 for predicting live birth from a blastocyst image of patients by age (66).…”

Embryo selection with artificial intelligence: how to evaluate and compare methods?

“…For instance, Fernandez et al [ 4 ] compare accuracy measures across different studies and datasets, without taking into account the different embryo populations and distributions of labels in the test sets. Miyagi et al [ 26 ] conclude that their predictive results are good, because their area under the curve (AUC) performance values on patient ages ≥ 38 years are higher compared to AUC values obtained across all age groups in a different study. Such comparisons are invalid, simply because the embryo populations in the different studies are different.…”

“…Many of the papers listed in Table 1 present comparisons of their AI models against embryologists on retrospective data without any mentions of biased performance considerations [ 12 – 15 , 20 , 22 , 24 , 26 ]. Some of these even claim statistical significant superiority over embryologists [ 12 , 13 , 20 ].…”