Preliminary study on objective evaluation algorithm of human infrared thermogram seriality and its clinical application in population with metabolic syndrome

Abstract

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.

Keywords: BMJ Health Informatics; Computing Methodologies; Decision Support Systems, Clinical.

Figure 1

Significant ROIs screening. ROI, Regions of Interest.

Figure 2

The flowchart of the seriality algorithm.

Figure 3

Schematic diagram of Infrared thermography data acquisition. MS, metabolic syndrome.

Figure 4

Comparison of significant ROIs and thermal sequence between healthy participants and patients with MS. (A) Average temperature distribution in three ROIs: healthy versus MS (gender-stratified). (B) Sequential distribution in healthy and MS (gender-stratified). MS, metabolic syndrome.

Figure 5

ROC curves of different genders on the training and the validation sets for MS. (A) ROC curves of the male training set and the validation set. (B)ROC curves of the female training set and the validation set. AUC, area under the curve.