Application of artificial neural networks in reproductive medicine

被引:5
作者
Yuan, Guanghui [1 ]
Lv, Bohan [2 ]
Hao, Cuifang [3 ,4 ]
机构
[1] Qingdao Univ, Dept Qingdao Med Coll, Qingdao, Shandong, Peoples R China
[2] Qingdao Univ, Dept Intens Care Unit, Affiliated Hosp, Qingdao, Shandong, Peoples R China
[3] Qingdao Univ, Dept Reprod Med, Affiliated Women & Childrens Hosp, Qingdao, Shandong, Peoples R China
[4] Qingdao Univ, Dept Reprod Med, Affiliated Women & Childrens Hosp, Qingdao 266011, Shandong, Peoples R China
关键词
Artificial intelligence; artificial neural networks; assisted reproduction; embryo quality assessment; personalised diagnosis and treatment; prediction model; INTELLIGENCE; PREDICTION; CANCER; CLASSIFICATION; EMBRYO; MODEL;
D O I
10.1080/14647273.2022.2156301
中图分类号
R71 [妇产科学];
学科分类号
100211 ;
摘要
With the emergence of the age of information, the data on reproductive medicine has improved immensely. Nonetheless, healthcare workers who wish to utilise the relevance and implied value of the various data available to aid clinical decision-making encounter the difficulty of statistically analysing such large data. The application of artificial intelligence becoming widespread in recent years has emerged as a turning point in this regard. Artificial neural networks (ANNs) exhibit beneficial characteristics of comprehensive analysis and autonomous learning, owing to which these are being applied to disease diagnosis, embryo quality assessment, and prediction of pregnancy outcomes. The present report aims to summarise the application of ANNs in the field of reproduction and analyse its further application potential.
引用
收藏
页码:1195 / 1201
页数:7
相关论文
共 47 条
[1]  
Akinsal EC, 2018, UROL J, V15, P122, DOI 10.22037/uj.v0i0.4029
[2]  
Berner ES, 2016, HEALTH INFORM SER, P1, DOI 10.1007/978-3-319-31913-1_1
[3]   An overview of the use of artificial neural networks in lung cancer research [J].
Bertolaccini, Luca ;
Solli, Piergiorgio ;
Pardolesi, Alessandro ;
Pasini, Antonello .
JOURNAL OF THORACIC DISEASE, 2017, 9 (04) :924-931
[4]   Performance of a deep learning based neural network in the selection of human blastocysts for implantation [J].
Bormann, Charles L. ;
Kanakasabapathy, Manoj Kumar ;
Thirumalaraju, Prudhvi ;
Gupta, Raghav ;
Pooniwala, Rohan ;
Kandula, Hemanth ;
Hariton, Eduardo ;
Souter, Irene ;
Dimitriadis, Irene ;
Ramirez, Leslie B. ;
Curchoe, Carol L. ;
Swain, Jason ;
Boehnlein, Lynn M. ;
Shafiee, Hadi .
ELIFE, 2020, 9
[5]  
Cao Q., 2020, MODERN ELECT TECHNIQ, V43, P74, DOI [10.16652/j.issn.1004-373x.2020.03.018, DOI 10.16652/J.ISSN.1004-373X.2020.03.018]
[6]   Implementing Machine Learning in Health Care - Addressing Ethical Challenges [J].
Char, Danton S. ;
Shah, Nigam H. ;
Magnus, David .
NEW ENGLAND JOURNAL OF MEDICINE, 2018, 378 (11) :981-983
[7]   Machine Learning and Deep Neural Networks Applications in Patient and Scan Preparation, Contrast Medium, and Radiation Dose Optimization [J].
Eberhard, Matthias ;
Alkadhi, Hatem .
JOURNAL OF THORACIC IMAGING, 2020, 35 :S17-S20
[8]   Dermatologist-level classification of skin cancer with deep neural networks [J].
Esteva, Andre ;
Kuprel, Brett ;
Novoa, Roberto A. ;
Ko, Justin ;
Swetter, Susan M. ;
Blau, Helen M. ;
Thrun, Sebastian .
NATURE, 2017, 542 (7639) :115-+
[9]   Artificial neural networks and risk stratification in emergency departments [J].
Falavigna, Greta ;
Costantino, Giorgio ;
Furlan, Raffaello ;
Quinn, James V. ;
Ungar, Andrea ;
Ippoliti, Roberto .
INTERNAL AND EMERGENCY MEDICINE, 2019, 14 (02) :291-299
[10]   Venous Thromboembolism in Women Undergoing Assisted Reproductive Technologies: Data from the RIETE Registry [J].
Grandone, Elvira ;
Di Micco, Pier Paolo ;
Villani, Michela ;
Colaizzo, Donatella ;
Fernandez-Capitan, Carmen ;
Del Toro, Jorge ;
Rosa, Vladimir ;
Bura-Riviere, Alessandra ;
Quere, Isabelle ;
Blanco-Molina, Angeles ;
Margaglione, Maurizio ;
Monreal, Manuel .
THROMBOSIS AND HAEMOSTASIS, 2018, 118 (11) :1962-1968