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AI in IVF and Embryology Lab

Edited by:
Associate Professor Zaher Merhi, MD, HCLD, Department of Obstetrics and Gynecology, Division of Reproductive Endocrinology and Infertility, Maimonides Medical Center and Rejuvenating Fertility Center, NY, United States of America
Associate Medical Director Marco Mouanness, MD, Division of Reproductive Endocrinology and Infertility, Rejuvenating Fertility Center, NY, United States of America

Submission Status: Closed   |   Submission Deadline: Closed

This collection is no longer accepting submissions.

Reproductive Biology and Endocrinology is calling for submissions to our collection on AI in IVF and Embryology Lab. Artificial intelligence (AI) is being increasingly used in various fields of medicine, including in vitro fertilization (IVF). IVF is a complex and expensive procedure that involves the use of various technologies to assist in the fertilization of human eggs with sperm outside the body. Overall, AI has the potential to revolutionize the field of IVF by improving the accuracy of embryo selection, personalizing treatment plans, and developing new technologies. However, more research is needed to fully understand the benefits and limitations of AI in IVF, and to ensure that its use is ethical and safe.

Image credit: Zaher Merhi

  1. To find the machine learning (ML) method that has the highest accuracy in predicting the semen quality of men based on basic questionnaire data about lifestyle behavior.

    Authors: Aykut Aykaç, Coşkun Kaya, Özer Çelik, Mehmet Erhan Aydın and Mustafa Sungur
    Citation: Reproductive Biology and Endocrinology 2024 22:112
    Content type: Research Published on:

  2. Dual-person inspection in IVF laboratories cannot fully avoid mix-ups or embryo transfer errors, and data transcription or entry is time-consuming and redundant, often leading to delays in completing medical r...

    Authors: Man-Xi Jiang, Lei Guo, Sen Li, Xiao-Feng Xiao, Wei Chen, Shao-Qing Chen, Nan-Qiao Chen, Yuan-Yuan Sun, Guang-Li Zhang, Xiao-Hai Zeng, Yan-Mei Xiao and Li-Hua Fan
    Citation: Reproductive Biology and Endocrinology 2024 22:96
    Content type: Methodology Published on:

    The Correction to this article has been published in Reproductive Biology and Endocrinology 2024 22:115

  3. To explore the optimal models for predicting the formation of high-quality embryos in Poor Ovarian Response (POR) Patients with Progestin-Primed Ovarian Stimulation (PPOS) using machine learning algorithms.

    Authors: Yu-Heng Xiao, Yu-Lin Hu, Xing-Yu Lv, Li-Juan Huang, Li-Hong Geng, Pu Liao, Yu-Bin Ding and Chang-Chun Niu
    Citation: Reproductive Biology and Endocrinology 2024 22:78
    Content type: Research Published on:

  4. The low live birth rate and difficult decision-making of the in vitro fertilization (IVF) treatment regimen bring great trouble to patients and clinicians. Based on the retrospective clinical data of patients ...

    Authors: Liu Liu, Hua Liang, Jing Yang, Fujin Shen, Jiao Chen and Liangfei Ao
    Citation: Reproductive Biology and Endocrinology 2024 22:76
    Content type: Research Published on:

  5. Prospective observational studies have demonstrated that the machine learning (ML) -guided noninvasive chromosome screening (NICS) grading system, which we called the noninvasive chromosome screening-artificia...

    Authors: Xiaoxi Li, Yaxin Yao, Dunmei Zhao, Xiufeng Chang, Yi Li, Huilan Lin, Huijuan Wei, Haiye Wang, Ying Mi, Lei Huang, Sijia Lu, Weimin Yang and Liyi Cai
    Citation: Reproductive Biology and Endocrinology 2024 22:61
    Content type: Research Published on:

  6. Deep learning has been increasingly investigated for assisting clinical in vitro fertilization (IVF). The first technical step in many tasks is to visually detect and locate sperm, oocytes, and embryos in imag...

    Authors: Jiaqi Wang, Yufei Jin, Aojun Jiang, Wenyuan Chen, Guanqiao Shan, Yifan Gu, Yue Ming, Jichang Li, Chunfeng Yue, Zongjie Huang, Clifford Librach, Ge Lin, Xibu Wang, Huan Zhao, Yu Sun and Zhuoran Zhang
    Citation: Reproductive Biology and Endocrinology 2024 22:59
    Content type: Methodology Published on:

  7. The best method for selecting embryos ploidy is preimplantation genetic testing for aneuploidies (PGT-A). However, it takes more labour, money, and experience. As such, more approachable, non- invasive techniq...

    Authors: Bing-Xin Ma, Guang-Nian Zhao, Zhi-Fei Yi, Yong-Le Yang, Lei Jin and Bo Huang
    Citation: Reproductive Biology and Endocrinology 2024 22:58
    Content type: Research Published on:

  8. The introduction of the time-lapse monitoring system (TMS) and the development of predictive algorithms could contribute to the optimal embryos selection for transfer. Therefore, the present study aims at inve...

    Authors: Myrto-Sotiria Papamentzelopoulou, Ilectra-Niki Prifti, Despoina Mavrogianni, Thomais Tseva, Ntilay Soyhan, Aikaterini Athanasiou, Antonia Athanasiou, Adamantios Athanasiou, Paraskevi Vogiatzi, George Konomos, Dimitrios Loutradis and Maria Sakellariou
    Citation: Reproductive Biology and Endocrinology 2024 22:27
    Content type: Research Published on:

  9. The quandary known as the Intracytoplasmic Sperm Injection (ICSI) paradox is found at the juncture of Assisted Reproductive Technology (ART) and ‘andrological ignorance’ – a term coined to denote the undervalu...

    Authors: Pallav Sengupta, Sulagna Dutta, Ravindran Jegasothy, Petr Slama, Chak-Lam Cho and Shubhadeep Roychoudhury
    Citation: Reproductive Biology and Endocrinology 2024 22:22
    Content type: Correspondence Published on:

  10. Several studies have demonstrated that iDAScore is more accurate in predicting pregnancy outcomes in cycles without preimplantation genetic testing for aneuploidy (PGT-A) compared to KIDScore and the Gardner c...

    Authors: Chun-I Lee, Chun-Chia Huang, Tsung-Hsien Lee, Hsiu-Hui Chen, En-Hui Cheng, Pin-Yao Lin, Tzu-Ning Yu, Chung-I Chen, Chien-Hong Chen and Maw-Sheng Lee
    Citation: Reproductive Biology and Endocrinology 2024 22:12
    Content type: Research Published on:

About the collection

Reproductive Biology and Endocrinology is calling for submissions to our collection on AI in IVF and Embryology Lab. Artificial intelligence (AI) is being increasingly used in various fields of medicine, including in vitro fertilization (IVF). IVF is a complex and expensive procedure that involves the use of various technologies to assist in the fertilization of human eggs with sperm outside the body. 

One area where AI is being used in IVF is in the prediction of embryo quality. In traditional IVF, embryologists visually examine embryos to determine which ones are the most viable for implantation. However, this process can be subjective and prone to human error. AI algorithms can analyze large amounts of data, such as images of embryos and patient data, to predict which embryos are most likely to result in a successful pregnancy. This can help to improve the accuracy of embryo selection, leading to higher success rates for IVF treatments.

Another application of AI in IVF is in the optimization of treatment protocols. AI algorithms can analyze patient data, such as age, medical history, and hormone levels, to personalize treatment plans for each individual. This can help to improve the efficacy of IVF treatments and reduce the risk of complications.

AI is also being used in the development of new IVF technologies. For example, researchers are developing AI-powered microscopes that can automatically identify and track sperm cells, which could help to improve the accuracy of IVF procedures.

Overall, AI has the potential to revolutionize the field of IVF by improving the accuracy of embryo selection, personalizing treatment plans, and developing new technologies. However, more research is needed to fully understand the benefits and limitations of AI in IVF, and to ensure that its use is ethical and safe.

Submission Guidelines

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This Collection welcomes submission of Research Articles, Data Notes, Case Reports, Study Protocols, and Database Articles.

Before submitting your manuscript, please ensure you have read our submission guidelines. Articles for this collection should be submitted via our submission system, Snapp. During the submission process you will be asked whether you are submitting to a collection, please select "AI in IVF and Embryology Lab" from the dropdown menu.

Articles will undergo the journal’s standard peer-review process and are subject to all of the journal’s standard policies. Articles will be added to the collection as they are published.

The Editors have no competing interests with the submissions which they handle through the peer review process. The peer review of any submissions for which the Editors have competing interests is handled by another Editorial Board Member who has no competing interests.