Facial Thermal Imaging + AI Accurately Predict Presence of Coronary Artery Disease

A combination of facial thermal imaging and artificial intelligence (AI) can accurately predict the presence of coronary artery disease, finds research published in the open access journal BMJ Health & Care Informatics.

This non-invasive real-time approach is more effective than conventional methods and could be adopted for clinical practice to improve the accuracy of diagnosis and workflow, pending testing on larger and more ethnically diverse numbers of patients, suggest the researchers.

Current guidelines for the diagnosis of coronary heart disease rely on probability assessment of risk factors which aren’t always very accurate or widely applicable, say the researchers.

And while these can be supplemented with other diagnostics, such as ECG readings, angiograms, and blood tests, these are often time consuming and invasive, they add.

Thermal imaging, which captures temperature distribution and variations on the object’s surface by detecting the infrared radiation emitted by that object, is non-invasive.

And it has emerged as a promising tool for disease assessment as it can identify areas of abnormal blood circulation and inflammation from skin temperature patterns.

The advent of machine learning technology (AI), with its capacity to extract, process, and integrate complex information, might enhance the accuracy and effectiveness of thermal imaging diagnostics.

The researchers therefore set out to look into the feasibility of using thermal imaging plus AI to accurately predict the presence of coronary artery disease without the need for invasive, time consuming techniques in 460 people with suspected heart disease.Their average age was 58; 126 (27.5%) of them were women.

Thermal images of their faces were captured before confirmatory examinations to develop and validate an AI assisted imaging model for detecting coronary artery disease.

In all, 322 participants (70%) were confirmed to have coronary artery disease. These people tended to be older and they were more likely to be men. They were also more likely to have lifestyle, clinical, and biochemical risk factors, as well as higher use of preventive meds.

The thermal imaging plus AI approach was around 13% better at predicting coronary artery disease than the pre-test risk assessment involving traditional risk factors and clinical signs and symptoms.

Among the three most significant predictive thermal indicators, the most influential was the overall left-right temperature difference of the face, followed by the maximal facial temperature, and average facial temperature.

And, specifically, the average temperature of the left jaw region was the strongest predictive feature, followed by the temperature range of the right eye region and the left-right temperature difference of the left temple regions.

The approach also effectively identified traditional risk factors for coronary artery disease: high cholesterol; male sex; smoking; excess weight (BMI); fasting blood glucose, as well as indicators of inflammation.

The researchers acknowledge the relatively small sample size of their study and the fact that it was carried out at only one centre. And the study participants had all been referred for confirmatory tests for suspected heart disease.

But they nevertheless write: "The feasibility of [thermal imaging] based [coronary artery disease] prediction suggests potential future applications and research opportunities."

They add: "As a biophysiological-based health assessment modality, [it] provides disease-relevant Information beyond traditional clinical measures that could enhance [atherosclerotic cardiovascular disease] and related chronic condition assessment.

"The non-contact, real-time nature of [it] allows for instant disease assessment at the point of care, which could streamline clinical workflows and save time for important physician–patient decision-making. In addition, it has the potential to enable mass prescreening."

And they conclude: "Our developed [thermal imaging] prediction models, based on advanced [machine learning] technology, have exhibited promising potential compared with the current conventional clinical tools.

"Further investigations incorporating larger sample sizes and diverse patient populations are needed to validate the external validity and generalisability of the current findings."

Kung M, Zeng J, Lin S, Yu X, Liu C, Shi M, Sun R, Yuan S, Lian X, Su X, Zhao Y, Zheng Z, Ji X.
Prediction of coronary artery disease based on facial temperature information captured by non-contact infrared thermography.
BMJ Health Care Inform. 2024 Jun 3;31(1):e100942. doi: 10.1136/bmjhci-2023-100942

Most Popular Now

AI can Help Improve Emergency Room Admis…

Generative artificial intelligence (AI), such as GPT-4, can help predict whether an emergency room patient needs to be admitted to the hospital even with only minimal training on a limited...

Philips ePatch and AI Analytics Platform…

Royal Philips (NYSE: PHG, AEX: PHIA), a global leader in health technology, announced the successful nationwide rollout of its ambulatory cardiac monitoring service in Spain using its unique wearable ePatch...

Comprehensive Bibliographic Dataset Adva…

A groundbreaking study published in Health Data Science, a Science Partner Journal, introduces a curated bibliographic dataset that aims to revolutionize the landscape of Health Artificial Intelligence (AI) research. Led...

AI Health Coach Lowers Blood Pressure an…

A new study in JMIR Cardio, published by JMIR Publications, shows that a fully digital, artificial intelligence (AI)-driven lifestyle coaching program can effectively reduce blood pressure (BP) in adults with...

Will Generative AI Change the Way Univer…

Since the launch of ChatGPT 3 in November 2022, we've been abuzz with talk of artificial intelligence: is it an unprecedented opportunity, or will it rob everyone of jobs and...

New Deep Learning Model is 'Game Ch…

Research led by the University of Plymouth has shown that a new deep learning AI model can identify what happens and when during embryonic development, from video. Published in the Journal...

Huge NHS Cloud Deals Mean Tough Question…

Opinion Article by Chris Scarisbrick, Deputy Managing Director, Sectra. The largest public cloud projects to ever take place within the NHS are beginning. Regional procurements for public cloud hosted diagnostic imaging...

AI Tech should Augment Physician Decisio…

The use of artificial intelligence (AI) in clinical health care has the potential to transform health care delivery but it should not replace physician decision-making, says the American College of...

A Three-Point Plan for Digital Delivery

Sam Shah has seen health tech policy up-close and worries that little progress has been made over the past five-years. However, he has a plan for any health and social...

Facial Thermal Imaging + AI Accurately P…

A combination of facial thermal imaging and artificial intelligence (AI) can accurately predict the presence of coronary artery disease, finds research published in the open access journal BMJ Health &...

New AI Algorithm Detects Rare Epileptic …

More than 3.4 million people in the US and 65 million people worldwide have epilepsy, a neurological disorder that affects the nervous system and causes seizures. One in 26 people...

Siemens Healthineers Debuts New Cardiolo…

Siemens Healthineers announces new cardiology applications with artificial intelligence for the Acuson Sequoia ultrasound system, as well as a new 4D transesophageal (TEE) transducer for cardiology exams. These cardiology applications...