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AI in Maternal Fetal Medicine and Perinatal Management
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AI in Maternal Fetal Medicine and Perinatal Management

A special issue of Journal of Clinical Medicine (ISSN 2077-0383). This special issue belongs to the section "Obstetrics & Gynecology".

Deadline for manuscript submissions: 5 December 2025 | Viewed by 3958

Special Issue Editor

Prof. Dr. Ariel Many

E-Mail Website
Guest Editor
Department of Obstetrics and Gynecology, Mayanei Hayeshua Medical Center, Bnei Brak 51544, Israel
Interests: preeclampsia; growth restriction; preterm birth; cesarean section; pregnancy complications; twins
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Maternal Fetal Medicine (MFM) is a specialized branch of obstetrics that focuses on the management of high-risk pregnancies and the care of both the mother and the fetus. Perinatal management refers to the overall care and support provided to pregnant women and their unborn babies, including prenatal care, monitoring the health and development of the fetus, managing complications and risks, and making decisions regarding the timing and mode of delivery. Artificial Intelligence (AI) is rapidly progressing in medicine, with applications ranging from imaging interpretation to decision support and much more.

This Special Issue, titled “AI in Maternal Fetal Medicine and Perinatal Management”, covers the implementation and use of various AI technologies as applied to a wide range of topics in perinatology and obstetrics; such topics include prediction models of adverse outcomes; applications related to ultrasound and other imaging techniques in maternal fetal medicine; hypertensive disorders; diabetes and other metabolic disorders; multiple pregnancies; fetal growth abnormalities or intrauterine growth restriction; preterm labor; and fetal congenital anomalies and genetic disorders.

Overall, AI is equipped to assist in Maternal Fetal Medicine and perinatal management to ensure the best possible care for high-risk pregnancies, promoting the health and well-being of both the mother and the fetus. We look forward to your contributions and submissions to this Special Issue and their role in supporting the ongoing advancement of AI in Maternal Fetal Medicine and perinatal management.

Prof. Dr. Ariel Many
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Journal of Clinical Medicine is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • maternal fetal medicine
  • high-risk pregnancy
  • perinatal management
  • artificial intelligence (AI) in obstetrics
  • fetal growth abnormalities
  • preterm labor prediction

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Published Papers (2 papers)

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9 pages, 194 KB  
Article
Perinatal Outcomes in Pregnancies Complicated by Maternal Thrombocytopenia: A Retrospective Cohort Study
by Woo Jeng Kim, In Yang Park and Sae Kyung Choi
J. Clin. Med. 2025, 14(13), 4524; https://doi.org/10.3390/jcm14134524 - 26 Jun 2025
Viewed by 845
Abstract
Background/Objectives: Maternal thrombocytopenia, affecting approximately 10% of pregnancies, may be physiological (e.g., gestational thrombocytopenia) or pathological (e.g., immune thrombocytopenic purpura, aplastic anemia, preeclampsia, systemic lupus erythematosus). While gestational thrombocytopenia is typically benign, its severity and etiology may impact maternal and neonatal outcomes. [...] Read more.
Background/Objectives: Maternal thrombocytopenia, affecting approximately 10% of pregnancies, may be physiological (e.g., gestational thrombocytopenia) or pathological (e.g., immune thrombocytopenic purpura, aplastic anemia, preeclampsia, systemic lupus erythematosus). While gestational thrombocytopenia is typically benign, its severity and etiology may impact maternal and neonatal outcomes. This study examined the association between severe and moderate thrombocytopenia during pregnancy and perinatal outcomes, focusing on maternal hemorrhage and neonatal thrombocytopenia. Methods: A retrospective analysis was conducted of 182 pregnant women with thrombocytopenia who delivered at Incheon St. Mary’s Hospital and Seoul St. Mary’s Hospital between 2009 and 2019. Participants were classified into two groups: severe thrombocytopenia (platelet count <50 × 109/L) and moderate thrombocytopenia (50–150 × 109/L). Maternal hemorrhagic outcomes, transfusion needs, and neonatal platelet counts were evaluated. Statistical analyses were performed using univariate methods. Results: Severe thrombocytopenia was associated with greater blood loss during delivery, increased transfusion requirements, and elevated neonatal thrombocytopenia rates. Moderate to severe thrombocytopenia was more frequently identified in neonates delivered by mothers with immune thrombocytopenic purpura than in those delivered by mothers with gestational thrombocytopenia. Conclusions: Both the severity and etiology of maternal thrombocytopenia significantly affect the risk of maternal hemorrhage and neonatal thrombocytopenia. Careful prenatal assessment is essential to optimize management and reduce complications. Full article
(This article belongs to the Special Issue AI in Maternal Fetal Medicine and Perinatal Management)

Review

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47 pages, 617 KB  
Review
Smart Pregnancy: AI-Driven Approaches to Personalised Maternal and Foetal Health—A Scoping Review
by Vera Correia, Teresa Mascarenhas and Miguel Mascarenhas
J. Clin. Med. 2025, 14(19), 6974; https://doi.org/10.3390/jcm14196974 - 1 Oct 2025
Cited by 1 | Viewed by 2702
Abstract
Background/Objectives: The integration of artificial intelligence (AI) into obstetric care poses significant potential to enhance clinical decision-making and optimize maternal and neonatal outcomes. Traditional prediction methods in maternal-foetal medicine often rely on subjective clinical judgment and limited statistical models, which may not [...] Read more.
Background/Objectives: The integration of artificial intelligence (AI) into obstetric care poses significant potential to enhance clinical decision-making and optimize maternal and neonatal outcomes. Traditional prediction methods in maternal-foetal medicine often rely on subjective clinical judgment and limited statistical models, which may not fully capture complex patient data. By integrating computational innovation with mechanistic biology and rigorous clinical validation, AI can finally fulfil the promise of precision obstetrics by transforming pregnancy complications into a preventable, personalised continuum of care. This study aims to map the current landscape of AI applications across the continuous spectrum of maternal–foetal health, identify the types of models used, and compare clinical targets and performance, potential pitfalls, and strategies to translate innovation into clinical impact. Methods: A literature search of peer-reviewed studies that employ AI for prediction, diagnosis, or decision support in Obstetrics was conducted. AI algorithms were categorised by application area: foetal monitoring, prediction of preterm birth, prediction of pregnancy complications, and/or labour and delivery. Results: AI-driven models consistently demonstrate superior performance to traditional approaches. Nevertheless, their widespread clinical adoption is hindered by limited dataset diversity, “black-box” algorithms, and inconsistent reporting standards. Conclusions: AI holds transformative potential to improve maternal and neonatal outcomes through earlier diagnosis, personalised risk assessment, and automated monitoring. To fulfil this promise, the field must prioritize the creation of large, diverse, open-access datasets, mandate transparent, explainable model architectures, and establish robust ethical and regulatory frameworks. By addressing these challenges, AI can become an integral, equitable, and trustworthy component of Obstetric care worldwide. Full article
(This article belongs to the Special Issue AI in Maternal Fetal Medicine and Perinatal Management)
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