Robust Automated Assessment of Human Blastocyst Quality using Deep Learning

Abstract

Morphology assessment has become the standard method for evaluation of embryo quality and selecting human blastocysts for transfer in in vitro fertilization (IVF). This process is highly subjective for some embryos and thus prone to human bias. As a result, morphological assessment results may vary extensively between embryologists and in some cases may fail to accurately predict embryo implantation and live birth potential. Here we postulated that an artificial intelligence (AI) approach trained on thousands of embryos can reliably predict embryo quality without human intervention.To test this hypothesis, we implemented an AI approach based on deep neural networks (DNNs). Our approach called STORK accurately predicts the morphological quality of blastocysts based on raw digital images of embryos with 98% accuracy. These results indicate that a DNN can automatically and accurately grade embryos based on raw images.Using clinical data for 2,182 embryos, we then created a decision tree that integrates 2 clinical parameters such as embryo quality and patient age to identify scenarios associated with increased or decreased pregnancy chance. This IVF data-driven analysis shows that the chance of pregnancy varies from 13.8% to 66.3%.In conclusion, our AI-driven approach provides a novel way to assess embryo quality and uncovers new, potentially personalized strategies to select embryos with an improved likelihood of pregnancy outcome.

Feasibility of deep learning for predicting live birth from a blastocyst image in patients classified by age

“…The accuracies of this method with deep learning have been published and were 0.997 for histopathological diagnosis of breast cancer, 0.90‐0.83 for the early diagnosis of Alzheimer's disease, 0.83 for urological dysfunctions, 0.72 and 0.50 for colposcopy, 0.83 for the diagnostic imaging of orthopedic trauma, and 0.98 for the morphological quality of blastocysts and evaluation by embryologist . In one report, embryos with fair‐quality images that were classified as poor and good quality were scored as 0.509 and 0.614, respectively, for the likelihood of achieving a positive live birth . In our study, the accuracy for predicting a live birth using images of the blastocyst when using the AI was 0.639, 0.708, 0.782, 0.807, and 0.881 for the age categories <35, 35‐37, 38‐39, 40‐41, and ≥42 years, respectively, as shown in Table .…”

“…69 In one report, embryos with fair-quality images that were classified as poor and good quality were scored as 0.509 and 0.614, respectively, for the likelihood of achieving a positive live birth. 69 In our study, the accuracy for predicting a live birth using images of the blastocyst when using the AI was 0.639, 0.708, 0.782, 0.807, and 0.881 for the age categories <35, 35-37, 38-39, 40-41, and ≥42 years, respectively, as shown in Table 5. Our results show that in spite of clinical impediment factors that are beyond images, factors such as uterine factors 70 seem to be average methods used in deep learning approaches to classify objects in medicine.…”

Using Deep Learning with Large Dataset of Microscope Images to Develop an Automated Embryo Grading System

“…It has been a time-consuming tasks and also may impose stress on embryo viability when taking out from the incubator for evaluation. Khosravi et al (2018) proposed another AIbased automated blastocyst assessment using time-lapse images, an emerging technology that allows continuous observation of embryo development without removing embryos from controlled and stable incubator conditions. They applied deep neural network using 10,148 images classified as good or poor quality with prediction accuracy of 98%.…”

“…Integrating the clinical information such as patients' clinical information along with IVF outcome into the prediction model could identify the scenarios associated with increased or decreased successful pregnancy. In addition, following the study of Khosravi et al (2018), connecting the automated grading system developed in this study with timelapse microscope system plus the interface of IVF Electronic Medical Record system, a fully-automated and non-invasive embryo grading system employing algorithms, device, and software would be achieved.…”

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).…”