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Researchers employ AI to investigate links between oral health and cardiovascular disease

A new research project is seeking to demonstrate how advanced machine learning and imaging analysis can illuminate markers embedded within routine scans that indicate risks for systemic diseases such as cardiovascular conditions. (Image: Sakina/Adobe Stock).

STONY BROOK, N.Y., US: The applicability and efficacy of artificial intelligence (AI) within dentistry appears to be limitless. With each passing day, scientists and researchers continue to illuminate new areas of clinical practice that can be radically transformed through AI-powered platforms. Nowhere is this seen more acutely than in the realm of diagnostics, where AI models are capable of instantaneously and accurately assessing a large body of visual data for a variety of conditions. At the same time as AI ushers in these major technological changes, the dental profession is also becoming increasingly aware of the manifold links that exist between oral health and systemic health and well-being.

Bringing these two potent threads together, an interdisciplinary team at Stony Brook University in the US has recently been awarded a prestigious grant to further their development of AI models capable of indicating the link between arterial calcifications and cardiovascular disease. The research team, composed of Dr Mina Mahdian, associate professor in the Stony Brook School of Dental Medicine’s Department of Prosthodontics and Digital Technology, and Dr Prateek Prasanna, assistant professor in the Department of Biomedical Informatics, received the US$300,000 (€277,000*) grant from the National Institute of Dental and Craniofacial Research for a two-year project entitled “Automated characterization of arterial calcification in dental cone beam computed tomography as predictors of cardiovascular disease”.

The project, which builds upon recently published research, seeks to develop a deep-learning AI model capable of analysing dental CBCT images for calcifications occurring in the extracranial carotid, intracranial carotid and vertebral arteries, a process which strongly suggests the likelihood of cardiovascular risks such as stroke or heart attack.

Speaking on the project in a university press release, Dr Mahdian said: “I am excited about this project, as this is the first study to apply quantitative imaging biomarkers, such as radiomics, to characterise vascular calcifications in CBCT to predict cardiovascular disease. The majority of current AI research is focused on common dental pathologies, whereas this project signifies the role of AI in assisting dental providers with predicting the risk of cardiovascular incidents based on dental CBCTs and making proper and timely referral.”

Echoing her thoughts, Dr Prasanna remarked: “This project is a prime example of how advanced machine learning and imaging analysis can reveal hidden signals in routine scans, like dental CBCTs, to inform risk for systemic diseases such as cardiovascular conditions. By bringing sophisticated computational tools to bear on everyday clinical data, we hope to enable earlier, more informed interventions, and showcase the broader impact AI can have across medicine.”

The potential functionality of such a model is undoubtedly great, since it could yield highly significant information about a patient’s cardiovascular profile from a standardised dental imaging procedure.

 

Editorial note:

* Calculated on the OANDA platform for 31 March 2025

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