Objectives
To develop an objective evaluation algorithm for assessing the seriality of infrared thermograms for the auxiliary diagnosis of diseases, and to internally validate the algorithm using metabolic syndrome (MS) as a case example.
Methods
A total of 266 healthy participants (133 of each sex) and 180 patients with MS (133 males and 47 females) were retrospectively enrolled. Infrared thermograms were randomly divided into a training set and a validation set at a ratio of 3:1. According to the algorithm proposed in this article, the thermal sequence values of patients with MS were calculated and compared between the two groups. The area under the curve (AUC) was computed to evaluate the diagnostic performance of thermogram seriality in MS detection.
Results
The established thermal sequence of healthy participants was as follows: T palm<T lower leg<T lower abdomen. In the training set, the AUCs for male and female patients with MS were 0.77 and 0.72, respectively, while in the validation set, they were 0.76 and 0.69, respectively. And results indicated that thermogram seriality demonstrated better diagnostic stability in males and in younger and middle-aged individuals. Additionally, higher body mass index values showed a positive correlation with increased thermal sequence values.
Discussion
The study proposed a novel, objective algorithm for quantitatively evaluating thermogram seriality. By focusing on temperature sequences rather than absolute temperature values, the algorithm is expected to facilitate a more quantitative evaluation of thermogram features. It could improve the stability and reproducibility of MS diagnosis.
Conclusion
The algorithm can quantitatively characterise thermogram seriality and can be used for the auxiliary screening of MS.
