The role of artificial intelligence in hypertensive disorders of pregnancy: towards personalized healthcare

ABSTRACT

Introduction

Guidelines advise ongoing follow-up of patients after hypertensive disorders of pregnancy (HDP) to assess cardiovascular risk and manage future patient-specific pregnancy conditions. However, there are limited tools available to monitor patients, with those available tending to be simple risk assessments that lack personalization. A promising approach could be the emerging artificial intelligence (AI)-based techniques, developed from big patient datasets to provide personalized recommendations for preventive advice.

Areas covered

In this narrative review, we discuss the impact of integrating AI and big data analysis for personalized cardiovascular care, focusing on the management of HDP.

Expert opinion

The pathophysiological response of women to pregnancy varies, and deeper insight into each response can be gained through a deeper analysis of the medical history of pregnant women based on clinical records and imaging data. Further research is required to be able to implement AI for clinical cases using multi-modality and multi-organ assessment, and this could expand both knowledge on pregnancy-related disorders and personalized treatment planning.

KEYWORDS:

• Hypertension disorders of pregnancy
• preeclampsia
• personalized medicine
• artificial intelligence
• machine learning
• deep learning

Article highlights

• Personalized cardiovascular care through AI techniques is crucial in managing HDP owing to its ability to handle big data in a personalized way and acquire non-linear relationships across multiple variables.

• AI has a high prognostic and diagnostic value when analyzing the clinical profile of pregnant women, which leaves its impact on proper and timely intervention to prevent disorders from developing and reduce morbidity and mortality rates.

• Researchers should focus more on the explain ability mechanisms of AI to derive machine-based information regarding HDP and its progression patterns.

• Further studies are still needed on assessing HDP using AI, which should target multiple imaging modalities across multiple organs in the body.

Declaration of interest

P Leeson acknowledges support from the Oxford NIHR Biomedical Research Centre, UKRI Medical Research Council, and the British Heart Foundation. The authors have no other relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript.

Reviewer disclosures

Peer reviewers on this manuscript have no relevant financial or other relationships to disclose.

Additional information

Funding

M. Alkhodari is funded by the Healthcare Engineering Innovation Center (HEIC) at Khalifa University, the United Arab Emirates (award number: 8474000132).