{"title":"使用延时视频对倍性状态进行分类的深度学习系统","authors":"Elena Paya M.Sc. , Cristian Pulgarín M.Sc. , Lorena Bori M.Sc. , Adrián Colomer Ph.D. , Valery Naranjo Ph.D. , Marcos Meseguer Ph.D.","doi":"10.1016/j.xfss.2023.06.002","DOIUrl":null,"url":null,"abstract":"<div><h3>Objective</h3><p>To develop a spatiotemporal model for de prediction of euploid and aneuploid<span> embryos using time-lapse videos from 10–115 hours after insemination (hpi).</span></p></div><div><h3>Design</h3><p>Retrospective study.</p></div><div><h3>Main Outcome Measures</h3><p>The research used an end-to-end approach to develop an automated artificial intelligence system capable of extracting features from images and classifying them, considering spatiotemporal dependencies. A convolutional neural network extracted the most relevant features from each video frame. A bidirectional long short-term memory layer received this information and analyzed the temporal dependencies, obtaining a low-dimensional feature vector that characterized each video. A multilayer perceptron classified them into 2 groups, euploid and noneuploid.</p></div><div><h3>Results</h3><p>The model performance in accuracy fell between 0.6170 and 0.7308. A multi-input model with a gate recurrent unit module performed better than others; the precision (or positive predictive value) is 0.8205 for predicting euploidy. Sensitivity, specificity, F1-Score and accuracy are 0.6957, 0.7813, 0.7042, and 0.7308, respectively.</p></div><div><h3>Conclusions</h3><p>This article proposes an artificial intelligence solution for prioritizing euploid embryo transfer. We can highlight the identification of a noninvasive method for chromosomal status diagnosis using a deep learning approach that analyzes raw data provided by time-lapse incubators. This method demonstrated potential automation of the evaluation process, allowing spatial and temporal information to encode.</p></div>","PeriodicalId":73012,"journal":{"name":"F&S science","volume":"4 3","pages":"Pages 211-218"},"PeriodicalIF":0.0000,"publicationDate":"2023-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Deep learning system for classification of ploidy status using time-lapse videos\",\"authors\":\"Elena Paya M.Sc. , Cristian Pulgarín M.Sc. , Lorena Bori M.Sc. , Adrián Colomer Ph.D. , Valery Naranjo Ph.D. , Marcos Meseguer Ph.D.\",\"doi\":\"10.1016/j.xfss.2023.06.002\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><h3>Objective</h3><p>To develop a spatiotemporal model for de prediction of euploid and aneuploid<span> embryos using time-lapse videos from 10–115 hours after insemination (hpi).</span></p></div><div><h3>Design</h3><p>Retrospective study.</p></div><div><h3>Main Outcome Measures</h3><p>The research used an end-to-end approach to develop an automated artificial intelligence system capable of extracting features from images and classifying them, considering spatiotemporal dependencies. A convolutional neural network extracted the most relevant features from each video frame. A bidirectional long short-term memory layer received this information and analyzed the temporal dependencies, obtaining a low-dimensional feature vector that characterized each video. A multilayer perceptron classified them into 2 groups, euploid and noneuploid.</p></div><div><h3>Results</h3><p>The model performance in accuracy fell between 0.6170 and 0.7308. A multi-input model with a gate recurrent unit module performed better than others; the precision (or positive predictive value) is 0.8205 for predicting euploidy. Sensitivity, specificity, F1-Score and accuracy are 0.6957, 0.7813, 0.7042, and 0.7308, respectively.</p></div><div><h3>Conclusions</h3><p>This article proposes an artificial intelligence solution for prioritizing euploid embryo transfer. We can highlight the identification of a noninvasive method for chromosomal status diagnosis using a deep learning approach that analyzes raw data provided by time-lapse incubators. This method demonstrated potential automation of the evaluation process, allowing spatial and temporal information to encode.</p></div>\",\"PeriodicalId\":73012,\"journal\":{\"name\":\"F&S science\",\"volume\":\"4 3\",\"pages\":\"Pages 211-218\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2023-08-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"F&S science\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S2666335X23000356\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"F&S science","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2666335X23000356","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Deep learning system for classification of ploidy status using time-lapse videos
Objective
To develop a spatiotemporal model for de prediction of euploid and aneuploid embryos using time-lapse videos from 10–115 hours after insemination (hpi).
Design
Retrospective study.
Main Outcome Measures
The research used an end-to-end approach to develop an automated artificial intelligence system capable of extracting features from images and classifying them, considering spatiotemporal dependencies. A convolutional neural network extracted the most relevant features from each video frame. A bidirectional long short-term memory layer received this information and analyzed the temporal dependencies, obtaining a low-dimensional feature vector that characterized each video. A multilayer perceptron classified them into 2 groups, euploid and noneuploid.
Results
The model performance in accuracy fell between 0.6170 and 0.7308. A multi-input model with a gate recurrent unit module performed better than others; the precision (or positive predictive value) is 0.8205 for predicting euploidy. Sensitivity, specificity, F1-Score and accuracy are 0.6957, 0.7813, 0.7042, and 0.7308, respectively.
Conclusions
This article proposes an artificial intelligence solution for prioritizing euploid embryo transfer. We can highlight the identification of a noninvasive method for chromosomal status diagnosis using a deep learning approach that analyzes raw data provided by time-lapse incubators. This method demonstrated potential automation of the evaluation process, allowing spatial and temporal information to encode.