An image processing protocol to extract variables predictive of human embryo fitness for assisted reproduction

Chéles, Dóris Spinosa; Ferreira, André Satoshi; Jesus, Isabela Sueitt de; Fernandez, Eleonora Inácio; Pinheiro, Gabriel Martins; Molin, Eloiza Adriane Dal; Alves, Wallace; Souza, Rebeca Colauto Milanezi de; Bori, Lorena; Meseguer, M.; Rocha, José Celso; Nogueira, Marcelo Fábio Gouveia

doi:10.1101/2022.02.01.22270264

Cited by 4 publications

References 44 publications

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deepBlastoid: A Deep Learning-Based High-Throughput Classifier for Human Blastoids Using Brightfield Images with Confidence Assessment

Fan,

Li,

Jin

et al. 2024

Preprint

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Recent advances in human blastoids have opened new avenues for modeling early human development and implantation. Human blastoids can be generated in large numbers, making them suitable for high-throughput screening, which often involves analyzing vast numbers of images. However, automated methods for evaluating and characterizing blastoid morphology are still underdeveloped. We developed a deep-learning model capable of recognizing and classifying blastoid brightfield images into five distinct quality categories. The model processes 53.2 images per second with an average accuracy of 87%, without signs of overfitting or batch effects. By integrating a Confidence Rate (CR) metric, the accuracy was further improved to 97%, with low-CR images flagged for human review. In a comparison with human experts, the model matched their accuracy while significantly outperforming them in throughput. We demonstrate the value of the model in two real-world applications: (1) systematic assessment of the effect of lysophosphatidic acid (LPA) concentration on blastoid formation, and (2) evaluating the impact of dimethyl sulfoxide (DMSO) on blastoids for drug screening. In the applications involving over 15,000 images, the model identified significant effects of LPA and DMSO, which may have been overlooked in manual assessments. The deepBlastoid model is publicly available and researchers can train their own model according to their imaging conditions and blastoid culture protocol. deepBlastoid thus offers a precise, automated approach for blastoid classification, with significant potential for advancing mechanism research, drug screening, and clinical in vitro fertilization (IVF) applications.

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deepBlastoid: A Deep Learning-Based High-Throughput Classifier for Human Blastoids Using Brightfield Images with Confidence Assessment

Fan,

Li,

Jin

et al. 2024

Preprint

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Image Processing Approach for Grading IVF Blastocyst: A State-of-the-Art Review and Future Perspective of Deep Learning-Based Models

Isa

Yusof

Zain³

2023

Applied Sciences

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The development of intelligence-based methods and application systems has expanded for the use of quality blastocyst selection in in vitro fertilization (IVF). Significant models on assisted reproductive technology (ART) have been discovered, including ones that process morphological image approaches and extract attributes of blastocyst quality. In this study, (1) the state-of-the-art in ART is established using an automated deep learning approach, applications for grading blastocysts in IVF, and related image processing techniques. (2) Thirty final publications in IVF and deep learning were found by an extensive literature search from databases using several relevant sets of keywords based on papers published in full-text English articles between 2012 and 2022. This scoping review sparks fresh thought in deep learning-based automated blastocyst grading. (3) This scoping review introduces a novel notion in the realm of automated blastocyst grading utilizing deep learning applications, showing that these automated methods can frequently match or even outperform skilled embryologists in particular deep learning tasks. This review adds to our understanding of the procedure for selecting embryos that are suitable for implantation and offers important data for the creation of an automated computer-based system for grading blastocysts that applies deep learning.

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Optimal Exposure Parameters for Microsurgery of Embryo Zona Pellucida Using Femtosecond Laser Pulses

Sitnikov,

Filatov,

Ilina

2023

Applied Sciences

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We applied femtosecond laser pulses for microsurgery of the zona pellucida (ZP) of mouse embryos in terms of assisted reproductive technologies. The embryos were subjected to a series of laser pulses (wavelength of 514 nm, pulse duration of 280 fs, repetition rate of 2.5 kHz). Optical microscopy was used to study the dependence of the ZP cut width on the pulse energy E and velocity v of the laser beam. It is shown that the same value of the cut width can be obtained for different combinations of these parameters. The boundaries of admissible values were found to be E = (19–52) nJ, v = (0.001–0.03) mm/s; recommendations on their proper choice are given. An analytical expression of the cut width of the ZP for a given combination of laser-pulse energy, beam velocity, and pulse repetition rate is proposed. This simple and easy-to-use equation allows for quick prediction of ZP cut width in embryo microsurgery procedures of laser-assisted hatching and embryo laser tagging using femtosecond laser pulses.

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An image processing protocol to extract variables predictive of human embryo fitness for assisted reproduction

Cited by 4 publications

References 44 publications

deepBlastoid: A Deep Learning-Based High-Throughput Classifier for Human Blastoids Using Brightfield Images with Confidence Assessment

deepBlastoid: A Deep Learning-Based High-Throughput Classifier for Human Blastoids Using Brightfield Images with Confidence Assessment

Image Processing Approach for Grading IVF Blastocyst: A State-of-the-Art Review and Future Perspective of Deep Learning-Based Models

Optimal Exposure Parameters for Microsurgery of Embryo Zona Pellucida Using Femtosecond Laser Pulses

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