Artificial Intelligence, Clinical Decision Support Algorithms, Mathematical Models, Calculators Applications in Infertility: Systematic Review and Hands-On Digital Applications

Carlo Bulletti, MD; Jason M. Franasiak, MD; Andrea Busnelli, MD; Romualdo Sciorio, BSc, Msc; Marco Berrettini, PhD; Lusine Aghajanova, MD, PhD; Francesco M. Bulletti, MD; Baris Ata, MD

Mayo Clinic Proceedings: Digital Health (Dec 2024)

Artificial Intelligence, Clinical Decision Support Algorithms, Mathematical Models, Calculators Applications in Infertility: Systematic Review and Hands-On Digital Applications

Carlo Bulletti, MD,
Jason M. Franasiak, MD,
Andrea Busnelli, MD,
Romualdo Sciorio, BSc, Msc,
Marco Berrettini, PhD,
Lusine Aghajanova, MD, PhD,
Francesco M. Bulletti, MD,
Baris Ata, MD

Affiliations

Carlo Bulletti, MD: Help Me Doctor, Assisted Reproductive Technology, Gynecological Endocrinology and Reproductive Surgery, Cattolica, Italy; Department of Obstetrics, Gynecology, and Reproductive Science, Yale University, New Haven, CT; Correspondence: Address to Carlo Bulletti, MD, Help Me Doctor, Via Nazario Sauro 30, 47841 Cattolica (RN), Italy.
Jason M. Franasiak, MD: IVI RMA, Reproductive Medicine Associates of New Jersey, NJ
Andrea Busnelli, MD: Department of Biomedical Sciences, Humanitas University, Milan, Italy; IRCCS Humanitas Research Hospital, Rozzano, Italy
Romualdo Sciorio, BSc, Msc: Fertility Medicine and Gynaecological Endocrinology Unit, Department Woman Mother Child, Lausanne University Hospital, CHUV, Lausanne, Switzerland
Marco Berrettini, PhD: Department of Statistical Sciences, University of Bologna, Bologna, Italy
Lusine Aghajanova, MD, PhD: Division of Reproductive Endocrinology and Infertility, Department of Obstetrics and Gynecology, Stanford University School of Medicine, Sunnyvale, CA
Francesco M. Bulletti, MD: Department Obstetrics and Gynecology, University Hospital of Vaud, Lausanne, Switzerland
Baris Ata, MD: ART Fertility Clinics, Dubai, United Arab Emirates; Department of Obstetrics and Gynecology, Koç University School of Medicine, Istanbul, Turkey

Journal volume & issue: Vol. 2, no. 4
pp. 518 – 532

Abstract

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The aim of this systematic review was to identify clinical decision support algorithms (CDSAs) proposed for assisted reproductive technologies (ARTs) and to evaluate their effectiveness in improving ART cycles at every stage vs traditional methods, thereby providing an evidence-based guidance for their use in ART practice. A literature search on PubMed and Embase of articles published between 1 January 2013 and 31 January 2024 was performed to identify relevant articles. Prospective and retrospective studies in English on the use of CDSA for ART were included. Out of 1746 articles screened, 116 met the inclusion criteria. The selected articles were categorized into 3 areas: prognosis and patient counseling, clinical management, and embryo assessment. After screening, 11 CDSAs were identified as potentially valuable for clinical management and laboratory practices. Our findings highlight the potential of automated decision aids to improve in vitro fertilization outcomes. However, the main limitation of this review was the lack of standardization in validation methods across studies. Further validation and clinical trials are needed to establish the effectiveness of these tools in the clinical setting.

Published in Mayo Clinic Proceedings: Digital Health

ISSN: 2949-7612 (Online)
Publisher: Elsevier
Country of publisher: United States
LCC subjects: Medicine: Medicine (General): Computer applications to medicine. Medical informatics
Website: https://www.sciencedirect.com/journal/mayo-clinic-proceedings-digital-health

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