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Wrenzycki C. Parameters to identify good quality oocytes and embryos in cattle. Reprod Fertil Dev 2021; 34:190-202. [PMID: 35231232 DOI: 10.1071/rd21283] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022] Open
Abstract
Oocyte/embryo selection methodologies are either invasive or noninvasive and can be applied at various stages of development from the oocyte to cleaved embryos and up to the blastocyst stage. Morphology and the proportion of embryos developing to the blastocyst stage are important criteria to assess developmental competence. Evaluation of morphology remains the method of choice for selecting viable oocytes for IVP or embryos prior to transfer. Although non-invasive approaches are improving, invasive ones have been extremely helpful in finding candidate genes to determine oocyte/embryo quality. There is still a strong need for further refinement of existing oocyte and embryo selection methods and quality parameters. The development of novel, robust and non-invasive procedures will ensure that only embryos with the highest developmental potential are chosen for transfer. In the present review, various methods for assessing the quality of oocytes and preimplantation embryos, particularly in cattle, are considered. These methods include assessment of morphology including different staining procedures, transcriptomic and proteomic analyses, metabolic profiling, as well as the use of artificial intelligence technologies.
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Affiliation(s)
- Christine Wrenzycki
- Chair for Molecular Reproductive Medicine, Clinic for Veterinary Obstetrics, Gynecology and Andrology of Large and Small Animals, Justus-Liebig-University Giessen, Frankfurter Straße 106, Giessen 35392, Germany
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Bori L, Dominguez F, Fernandez EI, Del Gallego R, Alegre L, Hickman C, Quiñonero A, Nogueira MFG, Rocha JC, Meseguer M. An artificial intelligence model based on the proteomic profile of euploid embryos and blastocyst morphology: a preliminary study. Reprod Biomed Online 2020; 42:340-350. [PMID: 33279421 DOI: 10.1016/j.rbmo.2020.09.031] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2020] [Revised: 09/17/2020] [Accepted: 09/30/2020] [Indexed: 12/30/2022]
Abstract
RESEARCH QUESTION The study aimed to develop an artificial intelligence model based on artificial neural networks (ANNs) to predict the likelihood of achieving a live birth using the proteomic profile of spent culture media and blastocyst morphology. DESIGN This retrospective cohort study included 212 patients who underwent single blastocyst transfer at IVI Valencia. A single image of each of 186 embryos was studied, and the protein profile was analysed in 81 samples of spent embryo culture medium from patients included in the preimplantation genetic testing programme. The information extracted from the analyses was used as input data for the ANN. The multilayer perceptron and the back-propagation learning method were used to train the ANN. Finally, predictive power was measured using the area under the curve (AUC) of the receiver operating characteristic curve. RESULTS Three ANN architectures classified most of the embryos correctly as leading (LB+) or not leading (LB-) to a live birth: 100.0% for ANN1 (morphological variables and two proteins), 85.7% for ANN2 (morphological variables and seven proteins), and 83.3% for ANN3 (morphological variables and 25 proteins). The artificial intelligence model using information extracted from blastocyst image analysis and concentrations of interleukin-6 and matrix metalloproteinase-1 was able to predict live birth with an AUC of 1.0. CONCLUSIONS The model proposed in this preliminary report may provide a promising tool to select the embryo most likely to lead to a live birth in a euploid cohort. The accuracy of prediction demonstrated by this software may improve the efficacy of an assisted reproduction treatment by reducing the number of transfers per patient. Prospective studies are, however, needed.
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Affiliation(s)
- Lorena Bori
- IVF laboratory, IVI Valencia, Valencia, Spain
| | - Francisco Dominguez
- IVI Foundation, Valencia, Instituto Universitario IVI (IUIVI), Valencia, Spain; Health Research Institute la Fe, Valencia, Spain.
| | | | - Raquel Del Gallego
- IVI Foundation, Valencia, Instituto Universitario IVI (IUIVI), Valencia, Spain
| | | | - Cristina Hickman
- Institute of Reproduction and Developmental Biology, Hammersmith Campus, Imperial College, London, UK
| | - Alicia Quiñonero
- IVI Foundation, Valencia, Instituto Universitario IVI (IUIVI), Valencia, Spain
| | | | - Jose Celso Rocha
- Universidade Estadual Paulista (Unesp), Faculdade de Ciências e Letras, Câmpus de Assis SP, Brazil
| | - Marcos Meseguer
- IVF laboratory, IVI Valencia, Valencia, Spain; Health Research Institute la Fe, Valencia, Spain
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Gouveia Nogueira MF, Bertogna Guilherme V, Pronunciate M, Dos Santos PH, Lima Bezerra da Silva D, Rocha JC. Artificial Intelligence-Based Grading Quality of Bovine Blastocyst Digital Images: Direct Capture with Juxtaposed Lenses of Smartphone Camera and Stereomicroscope Ocular Lens. SENSORS 2018; 18:s18124440. [PMID: 30558278 PMCID: PMC6308431 DOI: 10.3390/s18124440] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/11/2018] [Revised: 11/21/2018] [Accepted: 11/30/2018] [Indexed: 12/17/2022]
Abstract
In this study, we developed an online graphical and intuitive interface connected to a server aiming to facilitate professional access worldwide to those facing problems with bovine blastocysts classification. The interface Blasto3Q, where 3Q refers to the three qualities of the blastocyst grading, contains a description of 24 variables that were extracted from the image of the blastocyst and analyzed by three Artificial Neural Networks (ANNs) that classify the same loaded image. The same embryo (i.e., the biological specimen) was submitted to digital image capture by the control group (inverted microscope with 40× magnification) and the experimental group (stereomicroscope with maximum of magnification plus 4× zoom from the cell phone camera). The images obtained from the control and experimental groups were uploaded on Blasto3Q. Each image from both sources was evaluated for segmentation and submitted (only if it could be properly or partially segmented) for automatic quality grade classification by the three ANNs of the Blasto3Q program. Adjustments on the software program through the use of scaling algorithm software were performed to ensure the proper search and segmentation of the embryo in the raw images when they were captured by the smartphone, since this source produced small embryo images compared with those from the inverted microscope. With this new program, 77.8% of the images from smartphones were successfully segmented and from those, 85.7% were evaluated by the Blasto3Q in agreement with the control group.
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Affiliation(s)
- Marcelo Fábio Gouveia Nogueira
- Laboratory of Embryonic Micromanipulation, Department of Biological Sciences, School of Sciences and Languages, São Paulo State University (UNESP), Assis, São Paulo 19.806-900, Brazil.
- Multiuser Facility (FitoFarmaTec), Department of Pharmacology, Biosciences Institute, São Paulo State University (UNESP), Botucatu, São Paulo 18.618-689, Brazil.
| | - Vitória Bertogna Guilherme
- Laboratory of Embryonic Micromanipulation, Department of Biological Sciences, School of Sciences and Languages, São Paulo State University (UNESP), Assis, São Paulo 19.806-900, Brazil.
| | - Micheli Pronunciate
- Laboratory of Embryonic Micromanipulation, Department of Biological Sciences, School of Sciences and Languages, São Paulo State University (UNESP), Assis, São Paulo 19.806-900, Brazil.
- Multiuser Facility (FitoFarmaTec), Department of Pharmacology, Biosciences Institute, São Paulo State University (UNESP), Botucatu, São Paulo 18.618-689, Brazil.
| | - Priscila Helena Dos Santos
- Laboratory of Embryonic Micromanipulation, Department of Biological Sciences, School of Sciences and Languages, São Paulo State University (UNESP), Assis, São Paulo 19.806-900, Brazil.
- Multiuser Facility (FitoFarmaTec), Department of Pharmacology, Biosciences Institute, São Paulo State University (UNESP), Botucatu, São Paulo 18.618-689, Brazil.
| | - Diogo Lima Bezerra da Silva
- Laboratory of Applied Mathematics, Department of Biological Sciences, School of Sciences and Languages, São Paulo State University (UNESP), Assis, São Paulo 19.806-900, Brazil.
| | - José Celso Rocha
- Laboratory of Applied Mathematics, Department of Biological Sciences, School of Sciences and Languages, São Paulo State University (UNESP), Assis, São Paulo 19.806-900, Brazil.
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Optimization of quantum-inspired neural network using memetic algorithm for function approximation and chaotic time series prediction. Neurocomputing 2018. [DOI: 10.1016/j.neucom.2018.02.074] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
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A Method Based on Artificial Intelligence To Fully Automatize The Evaluation of Bovine Blastocyst Images. Sci Rep 2017; 7:7659. [PMID: 28794478 PMCID: PMC5550425 DOI: 10.1038/s41598-017-08104-9] [Citation(s) in RCA: 35] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2017] [Accepted: 07/06/2017] [Indexed: 11/28/2022] Open
Abstract
Morphological analysis is the standard method of assessing embryo quality; however, its inherent subjectivity tends to generate discrepancies among evaluators. Using genetic algorithms and artificial neural networks (ANNs), we developed a new method for embryo analysis that is more robust and reliable than standard methods. Bovine blastocysts produced in vitro were classified as grade 1 (excellent or good), 2 (fair), or 3 (poor) by three experienced embryologists according to the International Embryo Technology Society (IETS) standard. The images (n = 482) were subjected to automatic feature extraction, and the results were used as input for a supervised learning process. One part of the dataset (15%) was used for a blind test posterior to the fitting, for which the system had an accuracy of 76.4%. Interestingly, when the same embryologists evaluated a sub-sample (10%) of the dataset, there was only 54.0% agreement with the standard (mode for grades). However, when using the ANN to assess this sub-sample, there was 87.5% agreement with the modal values obtained by the evaluators. The presented methodology is covered by National Institute of Industrial Property (INPI) and World Intellectual Property Organization (WIPO) patents and is currently undergoing a commercial evaluation of its feasibility.
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Barchi AC, Ito S, Escaramboni B, Neto PDO, Herculano RD, Romeiro Miranda MC, Passalia FJ, Rocha JC, Fernández Núñez EG. Artificial intelligence approach based on near-infrared spectral data for monitoring of solid-state fermentation. Process Biochem 2016. [DOI: 10.1016/j.procbio.2016.07.017] [Citation(s) in RCA: 22] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
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