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Enhancing In Vitro Fertilization with Environment Optimization Utilizing Artificial Intelligence (EIVF-AI)
Malmö University, Faculty of Technology and Society (TS), Department of Computer Science and Media Technology (DVMT). Malmö University, Internet of Things and People (IOTAP).ORCID iD: 0000-0002-3797-4605
Malmö University, Faculty of Health and Society (HS), Department of Care Science (VV).ORCID iD: 0000-0002-4632-6175
Kristianstad University, Kristianstad, Sweden.
2025 (English)In: Pervasive Computing Technologies for Healthcare: 18th EAI International Conference, PervasiveHealth 2024, Heraklion, Crete, Greece, September 17–18, 2024, Proceedings, Part II / [ed] Haridimos Kondylakis; Andreas Triantafyllidis, Springer Nature , 2025, p. 151-158Conference paper, Published paper (Refereed)
Abstract [en]

In vitro fertilization (IVF) is of great aid to couples who are struggling to conceive. The IVF clinics, where couples undergo fertility treatments, require a carefully controlled environment to ensure the effectiveness of the procedures. In recent years, IVF has seen significant progress, thanks to new technologies and methods that improve success rates and expand options for infertile couples. One notable advancement involves combining pre-implantation genetic testing (PGT) with time-lapse imaging technology, which allows continuous monitoring of embryo development with minimal disturbance. This innovation improves the selection of healthy embryos for transfer, increasing success rates and reducing the risk of multiple pregnancies. However, maintaining a stable environment remains a key challenge. Fluctuations in temperature, humidity, air quality, and particulate matter can affect IVF success rates by disrupting the embryo’s delicate environment and potentially causing implantation failure. We discuss in this position paper our approach to alleviate such environmental problems in our project EIVF-AI funded by the Swedish funding agency Vinnova.

Place, publisher, year, edition, pages
Springer Nature , 2025. p. 151-158
Series
Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST, ISSN 1867-8211, E-ISSN 1867-822X ; 612
Keywords [en]
Artificial intelligence, In vitro fertilization (IVF), Machine Learning, Optimization
National Category
Gynaecology, Obstetrics and Reproductive Medicine
Identifiers
URN: urn:nbn:se:mau:diva-76111DOI: 10.1007/978-3-031-85575-7_8ISI: 001484285000008Scopus ID: 2-s2.0-105004255453ISBN: 978-3-031-85574-0 (print)ISBN: 978-3-031-85575-7 (print)OAI: oai:DiVA.org:mau-76111DiVA, id: diva2:1961423
Conference
18th EAI International Conference on Pervasive Computing Technologies for Healthcare, PervasiveHealth 2024, 17-18 Sep 2024, Heraklion, Crete, Greece
Available from: 2025-05-27 Created: 2025-05-27 Last updated: 2026-01-27Bibliographically approved

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Khoshkangini, RezaMangrio, Elisabeth

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Department of Computer Science and Media Technology (DVMT)Internet of Things and People (IOTAP)Department of Care Science (VV)
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