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Chen Y, Liu Y, Zuo X, Zhao Q, Sun M, Cui M, Zhao X, Du Y. Identification of significant imaging features for sensing oocyte viability. Microsc Res Tech 2023; 86:181-192. [PMID: 36278826 DOI: 10.1002/jemt.24248] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2022] [Revised: 09/26/2022] [Accepted: 10/06/2022] [Indexed: 01/21/2023]
Abstract
The evaluation of oocyte viability in the laboratory is limited to the morphological assessment by naked eyes, but the realization that most normal-appearing oocytes may conceal abnormalities prompts the search for automated approaches that can detect the abnormalities imperceptible to naked eyes. In this study, we developed an image processing pipeline applicable to bright-field microscope images to quantify the causal relationship between the quantitative imaging features and the developmental potential of oocytes. We acquired 19 imaging features of approximately 700 oocytes and determined two imaging subtypes, namely viable and nonviable subtypes that correlated closely with a viability fluorescence indicator and cleavage rates. The causal relationship between these imaging features and oocyte viability was derived from a viability-oriented Bayesian network that was developed based on the Bayesian information criterion and Tabu search. Our experimental results revealed that entropy with mean Gray Level Co-Occurrence Matrix energy describing the uniformity and texture roughness of cytoplasm were salient features for the automated selection of promising oocytes that exhibited excellent developmental potential.
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Affiliation(s)
- Yizhe Chen
- Institute of Robotics and Automatic Information System, College of Artificial Intelligence, Nankai University, Tianjin, China.,Tianjin Key Laboratory of Intelligent Robotics, Nankai University, Tianjin, China.,Institute of Intelligence Technology and Robotic Systems, Shenzhen Research Institute of Nankai University, Tianjin, China
| | - Yaowei Liu
- Institute of Robotics and Automatic Information System, College of Artificial Intelligence, Nankai University, Tianjin, China.,Tianjin Key Laboratory of Intelligent Robotics, Nankai University, Tianjin, China.,Institute of Intelligence Technology and Robotic Systems, Shenzhen Research Institute of Nankai University, Tianjin, China
| | - Xiaoying Zuo
- Institute of Robotics and Automatic Information System, College of Artificial Intelligence, Nankai University, Tianjin, China.,Tianjin Key Laboratory of Intelligent Robotics, Nankai University, Tianjin, China.,Institute of Intelligence Technology and Robotic Systems, Shenzhen Research Institute of Nankai University, Tianjin, China
| | - Qili Zhao
- Institute of Robotics and Automatic Information System, College of Artificial Intelligence, Nankai University, Tianjin, China.,Tianjin Key Laboratory of Intelligent Robotics, Nankai University, Tianjin, China.,Institute of Intelligence Technology and Robotic Systems, Shenzhen Research Institute of Nankai University, Tianjin, China
| | - Mingzhu Sun
- Institute of Robotics and Automatic Information System, College of Artificial Intelligence, Nankai University, Tianjin, China.,Tianjin Key Laboratory of Intelligent Robotics, Nankai University, Tianjin, China.,Institute of Intelligence Technology and Robotic Systems, Shenzhen Research Institute of Nankai University, Tianjin, China
| | - Maosheng Cui
- Institute of Intelligence Technology and Robotic Systems, Shenzhen Research Institute of Nankai University, Tianjin, China.,Innovation Team of Pig Feeding, Institute of Animal Science and Veterinary of Tianjin, Tianjin, China
| | - Xin Zhao
- Institute of Robotics and Automatic Information System, College of Artificial Intelligence, Nankai University, Tianjin, China.,Tianjin Key Laboratory of Intelligent Robotics, Nankai University, Tianjin, China.,Institute of Intelligence Technology and Robotic Systems, Shenzhen Research Institute of Nankai University, Tianjin, China
| | - Yue Du
- Institute of Robotics and Automatic Information System, College of Artificial Intelligence, Nankai University, Tianjin, China.,Tianjin Key Laboratory of Intelligent Robotics, Nankai University, Tianjin, China.,Institute of Intelligence Technology and Robotic Systems, Shenzhen Research Institute of Nankai University, Tianjin, China
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2
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Tokuoka Y, Yamada TG, Mashiko D, Ikeda Z, Kobayashi TJ, Yamagata K, Funahashi A. An explainable deep learning-based algorithm with an attention mechanism for predicting the live birth potential of mouse embryos. Artif Intell Med 2022; 134:102432. [PMID: 36462898 DOI: 10.1016/j.artmed.2022.102432] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2021] [Revised: 08/13/2022] [Accepted: 10/19/2022] [Indexed: 11/09/2022]
Abstract
In assisted reproductive technology (ART), embryos produced by in vitro fertilization (IVF) are graded according to their live birth potential, and high-grade embryos are preferentially transplanted. However, rates of live birth following clinical ART remain low worldwide. Grading is based on the embryo shape at a limited number of stages and does not consider the shape of embryos and intracellular structures, e.g., nuclei, at various stages important for normal embryogenesis. Here, we developed a Normalized Multi-View Attention Network (NVAN) that directly predicts live birth potential from the nuclear structure in live-cell fluorescence images of mouse embryos from zygote to across a wide range of stages. The input is morphological features of cell nuclei, which were extracted as multivariate time-series data by using the segmentation algorithm for mouse embryos. The classification accuracy of our method (83.87%) greatly exceeded that of existing machine-learning methods and that of visual inspection by embryo culture specialists. Our method also has a new attention mechanism that allows us to determine which values of multivariate time-series data, used to describe nuclear morphology, were the basis for the prediction. By visualizing the features that contributed most to the prediction of live birth potential, we found that the size and shape of the nucleus at the morula stage and at the time of cell division were important for live birth prediction. We anticipate that our method will help ART and developmental engineering as a new basic technology for IVF embryo selection.
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Affiliation(s)
- Yuta Tokuoka
- Center for Biosciences and Informatics, Graduate School of Fundamental Science and Technology, Keio University, 3-14-1 Hiyoshi, Kouhoku-ku, Yokohama, 223-8522, Japan
| | - Takahiro G Yamada
- Center for Biosciences and Informatics, Graduate School of Fundamental Science and Technology, Keio University, 3-14-1 Hiyoshi, Kouhoku-ku, Yokohama, 223-8522, Japan; Department of Biosciences and Informatics, Keio University, 3-14-1 Hiyoshi, Kouhoku-ku, Yokohama, 223-8522, Japan
| | - Daisuke Mashiko
- Faculty of Biology-Oriented Science and Technology, Kindai University, 930 Nishimitani, Kinokawa, Wakayama, 649-6493, Japan
| | - Zenki Ikeda
- Faculty of Biology-Oriented Science and Technology, Kindai University, 930 Nishimitani, Kinokawa, Wakayama, 649-6493, Japan
| | - Tetsuya J Kobayashi
- Institute of Industrial Science, The University of Tokyo, 4-6-1 Komaba, Meguro-ku, Tokyo, 153-8505, Japan
| | - Kazuo Yamagata
- Faculty of Biology-Oriented Science and Technology, Kindai University, 930 Nishimitani, Kinokawa, Wakayama, 649-6493, Japan
| | - Akira Funahashi
- Center for Biosciences and Informatics, Graduate School of Fundamental Science and Technology, Keio University, 3-14-1 Hiyoshi, Kouhoku-ku, Yokohama, 223-8522, Japan; Department of Biosciences and Informatics, Keio University, 3-14-1 Hiyoshi, Kouhoku-ku, Yokohama, 223-8522, Japan.
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3
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Erlich I, Ben-Meir A, Har-Vardi I, Grifo J, Wang F, Mccaffrey C, McCulloh D, Or Y, Wolf L. Pseudo contrastive labeling for predicting IVF embryo developmental potential. Sci Rep 2022; 12:2488. [PMID: 35169194 PMCID: PMC8847488 DOI: 10.1038/s41598-022-06336-y] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2021] [Accepted: 01/27/2022] [Indexed: 01/04/2023] Open
Abstract
In vitro fertilization is typically associated with high failure rates per transfer,
leading to an acute need for the identification of embryos with high developmental potential. Current methods are tailored to specific times after fertilization, often require expert inspection, and have low predictive power. Automatic methods are challenged by ambiguous labels, clinical heterogeneity, and the inability to utilize multiple developmental points. In this work, we propose a novel method that trains a classifier conditioned on the time since fertilization. This classifier is then integrated over time and its output is used to assign soft labels to pairs of samples. The classifier obtained by training on these soft labels presents a significant improvement in accuracy, even as early as 30 h post-fertilization. By integrating the classification scores, the predictive power is further improved. Our results are superior to previously reported methods, including the commercial KIDScore-D3 system, and a group of eight senior professionals, in classifying multiple groups of favorable embryos into groups defined as less favorable based on implantation outcomes, expert decisions based on developmental trajectories, and/or genetic tests.
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Affiliation(s)
- I Erlich
- The Alexender Grass Center for Bioengineering, School of Computer Science and Engineering, Hebrew University of Jerusalem, Jerusalem, Israel. .,Fairtilty Ltd., Tel Aviv, Israel.
| | - A Ben-Meir
- Fairtilty Ltd., Tel Aviv, Israel.,Infertility and IVF Unit, Department of Obstetrics and Gynecology, Hadassah Ein-Kerem Medical Center and Faculty of Medicine, Hebrew University of Jerusalem, Jerusalem, Israel
| | - I Har-Vardi
- Fairtilty Ltd., Tel Aviv, Israel.,Fertility and IVF Unit, Department of Obstetrics and Gynecology, Soroka University Medical Center and the Faculty of Health Sciences Ben-Gurion University of the Negev, Beer-Sheva, Israel
| | - J Grifo
- New York University Langone Prelude Fertility Center, New York, NY, USA
| | - F Wang
- New York University Langone Prelude Fertility Center, New York, NY, USA
| | - C Mccaffrey
- New York University Langone Prelude Fertility Center, New York, NY, USA
| | - D McCulloh
- New York University Langone Prelude Fertility Center, New York, NY, USA
| | - Y Or
- Fertility and IVF Unit, Obstetrics and Gynecology Division, Kaplan Medical Center, Rehovot, Israel
| | - L Wolf
- The School of Computer Science, Tel Aviv University, Tel Aviv, Israel
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Deep learning enables robust assessment and selection of human blastocysts after in vitro fertilization. NPJ Digit Med 2019; 2:21. [PMID: 31304368 PMCID: PMC6550169 DOI: 10.1038/s41746-019-0096-y] [Citation(s) in RCA: 181] [Impact Index Per Article: 36.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2018] [Accepted: 03/01/2019] [Indexed: 01/27/2023] Open
Abstract
Visual morphology assessment is routinely used for evaluating of embryo quality and selecting human blastocysts for transfer after in vitro fertilization (IVF). However, the assessment produces different results between embryologists and as a result, the success rate of IVF remains low. To overcome uncertainties in embryo quality, multiple embryos are often implanted resulting in undesired multiple pregnancies and complications. Unlike in other imaging fields, human embryology and IVF have not yet leveraged artificial intelligence (AI) for unbiased, automated embryo assessment. We postulated that an AI approach trained on thousands of embryos can reliably predict embryo quality without human intervention. We implemented an AI approach based on deep neural networks (DNNs) to select highest quality embryos using a large collection of human embryo time-lapse images (about 50,000 images) from a high-volume fertility center in the United States. We developed a framework (STORK) based on Google’s Inception model. STORK predicts blastocyst quality with an AUC of >0.98 and generalizes well to images from other clinics outside the US and outperforms individual embryologists. Using clinical data for 2182 embryos, we created a decision tree to integrate embryo quality and patient age to identify scenarios associated with pregnancy likelihood. Our analysis shows that the chance of pregnancy based on individual embryos varies from 13.8% (age ≥41 and poor-quality) to 66.3% (age <37 and good-quality) depending on automated blastocyst quality assessment and patient age. In conclusion, our AI-driven approach provides a reproducible way to assess embryo quality and uncovers new, potentially personalized strategies to select embryos.
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