Applications of Machine Learning in Obstetrics and Gynecology

Dear Colleagues,

The rapid growth of clinical data and the increasing demand for personalized medicine in women’s healthcare have accelerated the adoption of machine learning (ML) in obstetrics and gynecology (OB-GYN). As a journal dedicated to advancing diagnostic technologies, Diagnostics provides a timely and relevant forum via which to explore how ML can support early detection, risk stratification, and clinical decision-making in this critical medical domain.

This Special Issue aims to showcase recent advances in, and the innovative application of, ML in diagnostic processes across obstetrics and gynecology. We welcome high-quality submissions that demonstrate how machine learning contributes to the early identification of pregnancy-related complications, improves real-time maternal–fetal monitoring, enhances the sensitivity and specificity of cancer screening protocols, and supports precision diagnostics in reproductive medicine.

We are particularly interested in studies that integrate explainable artificial intelligence (XAI) with multimodal clinical data, including imaging, laboratory tests, and electronic health records. Manuscripts that present validated diagnostic tools, robust predictive models, or AI-assisted clinical decision support systems are especially encouraged.

The scope of this Special Issue includes, but is not limited to, the following topics:

• ML-based early detection of obstetric complications (e.g., preeclampsia, gestational diabetes);
• Predictive modeling for preterm birth, fetal growth restriction, and perinatal outcomes;
• Diagnostic image analysis using ML in obstetric and gynecologic imaging (ultrasound, CT, MRI);
• AI-assisted screening and diagnosis of gynecologic cancers (e.g., cervical, ovarian, endometrial);
• Applications of ML in diagnosing reproductive health conditions: PCOS, endometriosis, infertility;
• Natural language processing (NLP) for extracting diagnostic insights from OB-GYN records;
• Clinical decision support systems tailored to obstetric and gynecologic care;
• Integration of wearable sensors and remote monitoring in diagnostic frameworks;
• Development of explainable and interpretable ML models for clinical deployment;
• Validation of ML/AI algorithms in prospective or real-world clinical studies.

This Special Issue welcomes the submission of original research articles, comprehensive reviews, technical communications, and clinical case reports that highlight the potential of machine learning to enhance diagnostic accuracy, efficiency, and patient outcomes in obstetrics and gynecology.

Prof. Dr. Valentina Emilia Balas
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 100 words) can be sent to the Editorial Office for announcement on this website.

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. Diagnostics 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.

• machine learning
• obstetrics and gynecology
• pregnancy
• pregnancy-related complications
• reproductive medicine