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Zhang Q, Liang X, Chen Z. A review of artificial intelligence applications in in vitro fertilization. J Assist Reprod Genet 2025; 42:3-14. [PMID: 39400647 PMCID: PMC11806189 DOI: 10.1007/s10815-024-03284-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2024] [Accepted: 10/01/2024] [Indexed: 10/15/2024] Open
Abstract
The field of reproductive medicine has witnessed rapid advancements in artificial intelligence (AI) methods, which have significantly enhanced the efficiency of diagnosing and treating reproductive disorders. The integration of AI algorithms into the in vitro fertilization (IVF) has the potential to represent the next frontier in advancing personalized reproductive medicine and enhancing fertility outcomes for patients. The potential of AI lies in its ability to bring about a new era characterized by standardization, automation, and an improved success rate in IVF. At present, the utilization of AI in clinical practice is still in its early stages and faces numerous ethical, regulatory, and technical challenges that require attention. In this review, we present an overview of the latest advancements in various applications of AI in IVF, including follicular monitoring, oocyte assessment, embryo selection, and pregnancy outcome prediction. The aim is to reveal the current state of AI applications in the field of IVF, their limitations, and prospects for future development. Further studies, which involve the development of comprehensive models encompassing multiple functions and the conduct of large-scale randomized controlled trials, could potentially indicate the future direction of AI advancements in the field of IVF.
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Affiliation(s)
- Qing Zhang
- Key Laboratory of Medical Imaging Precision Theranostics and Radiation Protection, College of Hunan Province, The Affiliated Changsha Central Hospital, Hengyang Medical School, University of South China, Changsha, China
- Institute of Medical Imaging, Hengyang Medical School, University of South China, Hengyang, China
| | - Xiaowen Liang
- Key Laboratory of Medical Imaging Precision Theranostics and Radiation Protection, College of Hunan Province, The Affiliated Changsha Central Hospital, Hengyang Medical School, University of South China, Changsha, China
- Institute of Medical Imaging, Hengyang Medical School, University of South China, Hengyang, China
| | - Zhiyi Chen
- Key Laboratory of Medical Imaging Precision Theranostics and Radiation Protection, College of Hunan Province, The Affiliated Changsha Central Hospital, Hengyang Medical School, University of South China, Changsha, China.
- Institute of Medical Imaging, Hengyang Medical School, University of South China, Hengyang, China.
- Department of Medical Imaging, The Affiliated Changsha Central Hospital, Hengyang Medical School, University of South China, Changsha, China.
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Horta F, Salih M, Austin C, Warty R, Smith V, Rolnik DL, Reddy S, Rezatofighi H, Vollenhoven B. Reply: Artificial intelligence as a door opener for a new era of human reproduction. Hum Reprod Open 2023; 2023:hoad045. [PMID: 38033328 PMCID: PMC10686939 DOI: 10.1093/hropen/hoad045] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/02/2023] Open
Affiliation(s)
- F Horta
- Department of Obstetrics and Gynaecology, Monash University, Clayton, VIC, Australia
- Monash Data Future Institute, Monash University, Clayton, VIC, Australia
- City Fertility, Melbourne, VIC, Australia
| | - M Salih
- Department of Obstetrics and Gynaecology, Monash University, Clayton, VIC, Australia
- Department of Data Science and Artificial Intelligence, Faculty of Information Technology, Monash University, Clayton, VIC, Australia
| | - C Austin
- Department of Obstetrics and Gynaecology, Monash University, Clayton, VIC, Australia
- Department of Data Science and Artificial Intelligence, Faculty of Information Technology, Monash University, Clayton, VIC, Australia
| | - R Warty
- Department of Obstetrics and Gynaecology, Monash University, Clayton, VIC, Australia
| | - V Smith
- Department of Obstetrics and Gynaecology, Monash University, Clayton, VIC, Australia
| | - D L Rolnik
- Department of Obstetrics and Gynaecology, Monash University, Clayton, VIC, Australia
- Women’s and Newborn Program, Monash Health, Melbourne, VIC, Australia
| | - S Reddy
- School of Medicine, Deakin University, Geelong, VIC, Australia
| | - H Rezatofighi
- Monash Data Future Institute, Monash University, Clayton, VIC, Australia
- Department of Data Science and Artificial Intelligence, Faculty of Information Technology, Monash University, Clayton, VIC, Australia
| | - B Vollenhoven
- Department of Obstetrics and Gynaecology, Monash University, Clayton, VIC, Australia
- Women’s and Newborn Program, Monash Health, Melbourne, VIC, Australia
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