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Facial thermal imaging and AI accurately predict coronary heart disease
Last reviewed: 02.07.2025
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A study published in the journal BMJ Health & Care Informatics has found that a combination of facial thermal imaging and artificial intelligence (AI) can accurately predict coronary artery disease (CAD). The non-invasive, real-time method was found to be more effective than traditional methods and could be implemented in clinical practice to improve diagnostic accuracy and workflow, if tested in larger, more ethnically diverse patient populations, the researchers suggest.
Current guidelines for diagnosing coronary artery disease rely on risk factor probabilities, which are not always accurate or widely applicable, the researchers say. While these methods can be supplemented with other diagnostic tools, such as ECGs, angiograms and blood tests, they are often time-consuming and invasive, the researchers add.
Thermal imaging, which records the distribution and variations of temperature on the surface of an object by detecting infrared radiation, is non-invasive. It has proven itself as a promising tool for disease assessment, as it can identify areas of abnormal blood circulation and inflammation based on skin temperature patterns.
The advent of machine learning (AI) technologies with their ability to extract, process and integrate complex information can improve the accuracy and efficiency of thermal imaging diagnostics.
The researchers set out to investigate the possibility of using thermal imaging combined with AI to accurately predict the presence of coronary artery disease without the need for invasive and time-consuming methods in 460 people with suspected heart disease. Their average age was 58 years; 126 (27.5%) were women.
Thermal images of their faces were taken prior to confirmatory examinations to develop and validate an AI-assisted imaging model for detecting coronary artery disease.
A total of 322 participants (70%) had confirmed coronary heart disease. These individuals were generally older and more likely to be male. They were also more likely to have lifestyle, clinical, and biochemical risk factors, and to use preventive medications more frequently.
The approach using thermal imaging and AI was approximately 13% better at predicting coronary heart disease than a pre-assessment of risk using traditional risk factors and clinical signs and symptoms. Among the three most significant thermal indicators, the overall temperature difference between the left and right sides of the face was the most influential, followed by maximum facial temperature and average facial temperature.
In particular, the mean temperature of the left jaw region was the strongest predictor, followed by the temperature difference in the right eye region and the temperature difference between the left and right temples.
The approach also effectively identified traditional risk factors for coronary heart disease: high cholesterol, male gender, smoking, overweight (BMI), fasting glucose, and indicators of inflammation.
The researchers acknowledge the relatively small sample size of their study and the fact that it was conducted at only one center. In addition, all study participants were referred for confirmatory tests if they were suspected of having heart disease.
However, the team writes: "The ability of [thermal imaging] to predict [coronary artery disease] points to potential future applications and research opportunities... As a biophysiological method for assessing health, [it] provides disease-related information beyond traditional clinical measurements, which may improve the assessment of [atherosclerotic cardiovascular disease] and related chronic conditions."
"[Its] non-contact, real-time nature allows for instant disease assessment at the point of care, which can streamline clinical workflows and save time for important physician and patient decisions. It also has the potential for mass pre-screening."
The researchers conclude: "Our developed [thermal imaging] prediction models based on advanced [machine learning] technologies showed promising potential compared to current traditional clinical tools."
"Further studies involving larger numbers of patients and diverse populations are needed to confirm the external validity and generalizability of the current findings."