Deep learning neural network analysis of human blastocyst expansion from time-lapse image files

Huang, Tian-Sen; Kosasa, Thomas S.; Walker, Brienne; Arnett, C.M.; Huang, Christopher T.F.; Yin, Chelsea; Harun, Yousuf; Ahn, Hyeong Jun; Ohta, Aaron T.

doi:10.1016/j.rbmo.2021.02.015

Cited by 18 publications

(9 citation statements)

References 60 publications

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“…The problem of applying deep learning or machine learning techniques with little training data is over tting [22]. To enhance model robustness and minimize over tting, data augmentation techniques were employed using Robo ow, an advanced AI tool [4]. These techniques included image rotation, cropping, ipping, and bounding box creation.…”

Section: Background and Related Workmentioning

confidence: 99%

Advancing Embryo Selection: A Comparative Study of State-of-the-Art Deep Learning Models for IVF Embryo Quality Assessment

Tran,

Pham,

Nguyen

et al. 2024

Preprint

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This paper presents a comprehensive analysis of the application of deep learning models for embryo quality assessment in the field of in vitro fertilization (IVF). As embryo selection plays a crucial role in the success rates of IVF treatments, it is important to adopt an automated and accurate system to evaluate embryo viability. Our study focuses on comparing the effectiveness of four state-of-the-art deep learning models: VGG-19, EfficientNet, MobileNet, and ResNet, in classifying embryos based on their Inner cell mass (ICM) and trophectoderm (TE) cell characteristics from microscopic images. Utilizing a dataset of 1,020 embryonic images from two significant developmental stages distributed through the World Championship in Data Science and Artificial Intelligence 2023 - ISODS, we systematically trained and evaluated each model to address the challenges posed by small and imbalanced datasets commonly encountered in medical imaging. Through a series of experiments, including the application of data augmentation techniques and advanced model training strategies, we aimed to optimize model performance and minimize overfitting. The results revealed that EfficientNet-B0 has a high accuracy and robustness in distinguishing between viable and non-viable embryos. Additionally, we explored the potential of explainable AI techniques, such as Grad-CAM visualizations, to provide insights into the decision-making processes of the models. This study not only contributes to the advancement of automated embryo assessment systems but also lays the groundwork for future research to enhance IVF success rates through improved embryo selection methodologies.

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Section: Background and Related Workmentioning

confidence: 99%

Advancing Embryo Selection: A Comparative Study of State-of-the-Art Deep Learning Models for IVF Embryo Quality Assessment

Tran,

Pham,

Nguyen

et al. 2024

Preprint

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“…One unique approach to assessing BL quality is to evaluate a quantitative standard expansion assay (qSEA) utilizing AI. This measures the kinetics of BL expansion and correlates to outcome, where faster-expanding BLs exhibit higher implantation potential (Huang et al, 2021).…”

Section: Time-lapse Microscopy (Tlm) Image Analysismentioning

confidence: 99%

“…Manna et al, 2013;Sacha et al, 2021 Oocyte maturation assessment Automated identification of extruded polar body in metaphase II oocytes. Tran et al, 2019;Louis et al, 2021;Huang et al, 2021;Fitz et al, 2021 Predict ploidy status of embryo Noninvasive embryo ploidy prediction using static blastocyst stage embryo images and patient characteristics. Pennetta et al, 2018;Chavez-Badiola et al, 2020b;Meyer et al, 2020;Jiang et al, 2021 Quality assurance monitoring of laboratory procedures…”

Section: Oocyte Morphology Classificationmentioning

confidence: 99%

Artificial intelligence in the embryology laboratory: a review

Dimitriadis

Zaninović

Badiola

et al. 2022

Reproductive BioMedicine Online

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“…Deep networks are complex forms of ANN, and these AI technologies are widely used today in speech recognition, visual object recognition, and object detection, as well as in medical fields, such as drug discovery and genomics [ 35 ]. In RM, ANN are used in embryo segmentation [ 36 ], to describe blastocyst expansion and rank-order blastocysts for transfer [ 37 ], and to predict an overall outcome of IVF.…”

Section: Introductionmentioning

confidence: 99%

The Future Is Coming: Artificial Intelligence in the Treatment of Infertility Could Improve Assisted Reproduction Outcomes—The Value of Regulatory Frameworks

Medenica

Zivanovic²,

Batkoska

et al. 2022

Diagnostics

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Infertility is a global health issue affecting women and men of reproductive age with increasing incidence worldwide, in part due to greater awareness and better diagnosis. Assisted reproduction technologies (ART) are considered the ultimate step in the treatment of infertility. Recently, artificial intelligence (AI) has been progressively used in the many fields of medicine, integrating knowledge and computer science through machine learning algorithms. AI has the potential to improve infertility diagnosis and ART outcomes estimated as pregnancy and/or live birth rate, especially with recurrent ART failure. A broad-ranging review has been conducted, focusing on clinical AI applications up until September 2022, which could be estimated in terms of possible applications, such as ultrasound monitoring of folliculogenesis, endometrial receptivity, embryo selection based on quality and viability, and prediction of post implantation embryo development, in order to eliminate potential contributing risk factors. Oocyte morphology assessment is highly relevant in terms of successful fertilization rate, as well as during oocyte freezing for fertility preservation, and substantially valuable in oocyte donation cycles. AI has great implications in the assessment of male infertility, with computerised semen analysis systems already in use and a broad spectrum of possible AI-based applications in environmental and lifestyle evaluation to predict semen quality. In addition, considerable progress has been made in terms of harnessing AI in cases of idiopathic infertility, to improve the stratification of infertile/fertile couples based on their biological and clinical signatures. With AI as a very powerful tool of the future, our review is meant to summarise current AI applications and investigations in contemporary reproduction medicine, mainly focusing on the nonsurgical aspects of it; in addition, the authors have briefly explored the frames of reference and guiding principles for the definition and implementation of legal, regulatory, and ethical standards for AI in healthcare.

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Deep learning neural network analysis of human blastocyst expansion from time-lapse image files

Cited by 18 publications

References 60 publications

Advancing Embryo Selection: A Comparative Study of State-of-the-Art Deep Learning Models for IVF Embryo Quality Assessment

Advancing Embryo Selection: A Comparative Study of State-of-the-Art Deep Learning Models for IVF Embryo Quality Assessment

Artificial intelligence in the embryology laboratory: a review

The Future Is Coming: Artificial Intelligence in the Treatment of Infertility Could Improve Assisted Reproduction Outcomes—The Value of Regulatory Frameworks

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