Digital technologies and artificial intelligence in the diagnosis of cardiovascular complications in pregnancy: a review

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Abstract

Cardiovascular diseases during pregnancy remain a leading cause of maternal morbidity and mortality worldwide. The development of digital technologies and artificial intelligence offers new opportunities to improve risk stratification, early diagnosis, and monitoring of cardiovascular complications in pregnant women. Although effective, conventional diagnostic approaches, including electrocardiography, echocardiography, and biochemical markers are often limited by sensitivity, reproducibility, and feasibility for immediate use during pregnancy. Artificial intelligence models integrating multimodal data—clinical history, imaging, laboratory values, and wearable data—demonstrate the potential to detect subclinical changes that may remain unrecognized using standard diagnostic approaches. Emerging evidence supports the effectiveness of artificial intelligence in detecting arrhythmias, diagnosing peripartum cardiomyopathy, assessing valvular heart disease, and predicting cardiovascular risk and hypertensive disorders of pregnancy, including preeclampsia. Neural network–based models have shown advantages over traditional statistical methods, achieving high predictive accuracy (with areas under the ROC curve > 0.90 in some studies). Furthermore, the use of artificial intelligence in the interpretation of medical imaging and phonocardiographic recordings may reduce inter-observer variability and enhance diagnostic efficiency. Despite these promising findings, significant challenges remain, including data quality, algorithmic bias, ethical considerations, regulatory constraints, and limited clinical validation in pregnant populations. Responsible integration of artificial intelligence into obstetric and cardiovascular practice requires interdisciplinary collaboration, rigorous validation, and transparent control.

In summary, artificial intelligence technologies possess a transformative potential for optimizing the management of pregnant women with cardiovascular disease and may contribute to reducing maternal morbidity and mortality, provided that ethical and organizational barriers are adequately addressed.

About the authors

Yurii A. Trusov

Samara State Medical University

Email: yu.a.trusov@samsmu.ru
ORCID iD: 0000-0001-6407-3880
SPIN-code: 3203-5314
Russian Federation, Samara

Khadizhat T. Shamsueva

North-Ossetian State Medical Academy

Email: shamsuevakh1@gmail.com
ORCID iD: 0009-0003-7173-9072
Russian Federation, Vladikavkaz

Marianna Z. Kolkhidova

North-Ossetian State Medical Academy

Email: mari_kolxi@mail.ru
ORCID iD: 0009-0003-1301-8795
Russian Federation, Vladikavkaz

Albina T. Inderbieva

North-Ossetian State Medical Academy

Email: dr.inderbieva@mail.ru
ORCID iD: 0009-0007-8620-4790
Russian Federation, Vladikavkaz

Elizaveta D. Baryshnikova

The Russian National Research Medical University named after N.I. Pirogov (Pirogov University)

Email: mazhirinal2013@yandex.ru
ORCID iD: 0009-0007-6066-9354
Russian Federation, Moscow

Kamilya A. Khusnutdinova

The Russian National Research Medical University named after N.I. Pirogov (Pirogov University)

Email: Khusnutdinova.k@mail.ru
ORCID iD: 0009-0004-7935-9477
Russian Federation, Moscow

Aleksandra E. Rasponomareva

Professor V.F. Voino-Yasenetsky Krasnoyarsk State Medical University

Author for correspondence.
Email: aleksandrarasp@mail.ru
ORCID iD: 0009-0008-3382-1493
Russian Federation, Krasnoyarsk

Alina R. Shabazgerieva

North-Ossetian State Medical Academy

Email: alyashabazgerieva@gmail.com
ORCID iD: 0009-0003-5687-7098
Russian Federation, Vladikavkaz

Khadzhimurad K. Radzhabov

Penza State University

Email: khadzhimurad.radzhabov@mail.ru
ORCID iD: 0009-0004-3169-951X
Russian Federation, Penza

Stanislav A. Sanakoev

North-Ossetian State Medical Academy

Email: stas.sanakoev-2018@mail.ru
ORCID iD: 0009-0003-1058-9226
Russian Federation, Vladikavkaz

Valeria Kh. Kudzieva

Rostov State Medical University

Email: kudzievavaleria@mail.ru
ORCID iD: 0009-0006-6371-6112
Russian Federation, Rostov-on-Don

Alina V. Ponomareva

Kuban State Medical University

Email: alipon4@yandex.ru
ORCID iD: 0009-0004-3936-7982
Russian Federation, Krasnodar

Natalia K. Kozyreva

Kuban State Medical University

Email: Nata05042004@yandex.ru
ORCID iD: 0009-0003-1517-0539
Russian Federation, Krasnodar

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