scholarly journals Performance of a deep learning based neural network in the selection of human blastocysts for implantation

eLife ◽  
2020 ◽  
Vol 9 ◽  
Author(s):  
Charles L Bormann ◽  
Manoj Kumar Kanakasabapathy ◽  
Prudhvi Thirumalaraju ◽  
Raghav Gupta ◽  
Rohan Pooniwala ◽  
...  
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Time Lapse ◽  
Vitro Fertilization ◽  
Clinical Patient ◽  
Static Images ◽  
Transfer Order ◽  
Selection Of

Deep learning in in vitro fertilization is currently being evaluated in the development of assistive tools for the determination of transfer order and implantation potential using time-lapse data collected through expensive imaging hardware. Assistive tools and algorithms that can work with static images, however, can help in improving the access to care by enabling their use with images acquired from traditional microscopes that are available to virtually all fertility centers. Here, we evaluated the use of a deep convolutional neural network (CNN), trained using single timepoint images of embryos collected at 113 hr post-insemination, in embryo selection amongst 97 clinical patient cohorts (742 embryos) and observed an accuracy of 90% in choosing the highest quality embryo available. Furthermore, a CNN trained to assess an embryo’s implantation potential directly using a set of 97 euploid embryos capable of implantation outperformed 15 trained embryologists (75.26% vs. 67.35%, p<0.0001) from five different fertility centers.

scholarly journals Using Time Lapse Monitoring for Determination of Morphological Defect Frequency in Feline Embryos after in Vitro Fertilization (IVF)

Animals ◽  
2019 ◽  
Vol 10 (1) ◽  
pp. 3 ◽  
Author(s):  
Barbara Kij ◽  
Joanna Kochan ◽  
Agnieszka Nowak ◽  
Wojciech Niżański ◽  
Sylwia Prochowska ◽  
...  
Keyword(s):  
Time Lapse ◽  
Blastocyst Stage ◽  
Hatching Rate ◽  
Vitro Fertilization ◽  
First Results ◽  
Morphological Defects ◽  
Defect Frequency ◽  
Selection Of

Some human, bovine, and mouse in vitro fertilized (IVF) embryos with morphokinetic abnormalities such as fragmentation, direct cleavage, and cytoplasmic vacuoles have the potential to reach the blastocyst stage, which is related to a high potential for implantation. The latest techniques of embryo development observation to enable the evaluation and selection of embryos are based on time lapse monitoring (TLM). The aim of this study was to determine the frequency of morphological defects in feline embryos, their competence to reach the blastocyst stage, and their ability to hatch. Oocyte-cumulus complexes were isolated after the scarification of ovaries and matured in vitro. Matured oocytes were fertilized in vitro by capacitated spermatozoa. Randomly selected oocytes were observed by TLM for seven-to-eight days. Out of 76 developed embryos, 41 were morphologically normal, of which 15 reached the blastocyst stage. Of 35 abnormally developed embryos, 17 reached the blastocyst stage, of which six had single aberrations and 11 had multiple aberrations. The hatching rate (%) was 15.6% in normally cleaving embryos, 6.25% in embryos with single aberrations, and 3.33% in those with multiple aberrations. The present study reports the first results, found by using TLM, about the frequency of the morphological defects of feline embryos, their competence to reach the blastocyst stage, and their ability to hatch.

Time-Lapse Systems: A Comprehensive Analysis on Effectiveness

2022 ◽  
Author(s):  
Patricia Fadon ◽  
Eleanor Gallegos ◽  
Salonika Jalota ◽  
Lourdes Muriel ◽  
Cesar Diaz-Garcia
Keyword(s):  
Time Lapse ◽  
Culture Conditions ◽  
Comprehensive Analysis ◽  
Computer Assisted ◽  
Treatment Interventions ◽  
Vitro Fertilization ◽  
Recent Computer ◽  
Conventional Technology ◽  
Selection Of

AbstractTime-lapse systems have quickly become a common feature of in vitro fertilization laboratories all over the world. Since being introduced over a decade ago, the alleged benefits of time-lapse technology have continued to grow, from undisturbed culture conditions and round the clock, noninvasive observations to more recent computer-assisted selection of embryos through the development of algorithms. Despite the global uptake of time-lapse technology, its real impact on clinical outcomes is still controversial. This review aims to explore the different features offered by time-lapse technology, discussing incubation, algorithms, artificial intelligence and the regulation of nonessential treatment interventions, while assessing evidence on whether any benefit is offered over conventional technology.

scholarly journals Deep learning enables robust assessment and selection of human blastocysts after in vitro fertilization

2019 ◽  
Vol 2 (1) ◽  
Author(s):  
Pegah Khosravi ◽  
Ehsan Kazemi ◽  
Qiansheng Zhan ◽  
Jonas E. Malmsten ◽  
Marco Toschi ◽  
...  
Keyword(s):  
Deep Learning ◽  
In Vitro Fertilization ◽  
Vitro Fertilization ◽  
Selection Of

scholarly journals Solving the "right" problems for effective machine learning driven in vitro fertilization

2021 ◽  
Author(s):  
Itay Erlich ◽  
Assaf Ben-Meir ◽  
Iris Har-Vardi ◽  
James A Grifo ◽  
Assaf Zaritsky
Keyword(s):  
Machine Learning ◽  
In Vitro Fertilization ◽  
Clinical Factors ◽  
Vitro Fertilization ◽  
Data Intensive ◽  
Live Embryo ◽  
The Right ◽  
Visual Properties ◽  
Selection Of

Automated live embryo imaging has transformed in-vitro fertilization (IVF) into a data-intensive field. Unlike clinicians who rank embryos from the same IVF cycle cohort based on the embryos visual quality and determine how many embryos to transfer based on clinical factors, machine learning solutions usually combine these steps by optimizing for implantation prediction and using the same model for ranking the embryos within a cohort. Here we establish that this strategy can lead to sub-optimal selection of embryos. We reveal that despite enhancing implantation prediction, inclusion of clinical properties hampers ranking. Moreover, we find that ambiguous labels of failed implantations, due to either low quality embryos or poor clinical factors, confound both the optimal ranking and even implantation prediction. To overcome these limitations, we propose conceptual and practical steps to enhance machine-learning driven IVF solutions. These consist of separating the optimizing of implantation from ranking by focusing on visual properties for ranking, and reducing label ambiguity.

scholarly journals Deep Learning Based Proarrhythmia Analysis Using Field Potentials Recorded from Human Pluripotent Stem Cells Derived Cardiomyocytes

2018 ◽  
Author(s):  
Zeinab Golgooni ◽  
Sara Mirsadeghi ◽  
Mahdieh Soleymani Baghshah ◽  
Pedram Ataee ◽  
Hossein Baharvand ◽  
...  
Keyword(s):  
Neural Network ◽  
Stem Cells ◽  
Deep Learning ◽  
Pluripotent Stem Cells ◽  
Field Potential ◽  
Input Sequence ◽  
Patient Specific ◽  
Field Potentials ◽  
Drug Induced

AbstractAimAn early characterization of drug-induced cardiotoxicity may be possible by combining comprehensive in vitro pro-arrhythmia assay and deep learning techniques. The goal of this study was to develop a deep learning method to automatically detect irregular beating rhythm as well as abnormal waveforms of field potentials in an in vitro cardiotoxicity assay using human pluripotent stem cell (hPSC) derived cardiomyocytes and multi-electrode array (MEA) system.Methods and ResultsWe included field potential waveforms from 380 experiments which obtained by application of some cardioactive drugs on healthy and/or patient-specific induced pluripotent stem cells derived cardiomyocytes (iPSC-CM). We employed convolutional and recurrent neural networks, in order to develop a new method for automatic classification of field potential recordings without using any hand-engineered features. In the proposed method, a preparation phase was initially applied to split 60-second long recordings into a series of 5-second long windows. Thereafter, the classification phase comprising of two main steps was designed. In the first step, 5-second long windows were classified using a designated convolutional neural network (CNN). In the second step, the results of 5-second long window assessments were used as the input sequence to a recurrent neural network (RNN). The output was then compared to electrophysiologist-level arrhythmia (irregularity or abnormal waveforms) detection, resulting in 0.84 accuracy, 0.84 sensitivity, 0.85 specificity, and 0.88 precision.ConclusionA novel deep learning approach based on a two-step CNN-RNN method can be used for automated analysis of “irregularity or abnormal waveforms” in an in vitro model of cardiotoxicity experiments.

Sign in / Sign up

Export Citation Format

Share Document