Current guidelines for diagnosing coronary heart disease often rely on probability assessments of risk factors, which are not always accurate or universally applicable. These assessments can be supplemented with diagnostics such as ECG readings, angiograms, and blood tests, but these methods are often time-consuming and invasive. Thermal imaging, which detects infrared radiation emitted from an object's surface to capture temperature distribution and variations, is non-invasive. It has emerged as a promising tool for disease assessment by identifying areas of abnormal blood circulation and inflammation from skin temperature patterns. The integration of machine learning (AI), with its ability to extract, process, and integrate complex information, might improve the accuracy and effectiveness of thermal imaging diagnostics. New research has shown that a combination of facial thermal imaging and AI can accurately predict the presence of coronary artery disease.

Researchers at Tsinghua University (Beijing, China) examined the feasibility of using thermal imaging and AI to predict coronary artery disease without invasive, time-consuming techniques in 460 individuals with suspected heart disease. The participants had an average age of 58, and 126 (27.5%) were women. Thermal images of their faces were taken before confirmatory examinations to develop and validate an AI-assisted imaging model for detecting coronary artery disease. A total of 322 participants (70%) were confirmed to have coronary artery disease. These individuals were generally older, more likely to be men, and more likely to have lifestyle, clinical, and biochemical risk factors, as well as higher usage of preventive medications.

The thermal imaging and AI approach was about 13% more effective at predicting coronary artery disease than pre-test risk assessments involving traditional risk factors and clinical signs and symptoms. Among the three most significant predictive thermal indicators, the overall left-right temperature difference of the face was the most influential, followed by the maximal facial temperature and average facial temperature. 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. This approach also effectively identified traditional risk factors for coronary artery disease, such as high cholesterol, male sex, smoking, excess weight (BMI), fasting blood glucose, and indicators of inflammation.

“The feasibility of [thermal imaging] based [coronary artery disease] prediction suggests potential future applications and research opportunities,” stated the researchers. “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.” 

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