Hyperthermia associated with spinal radiculopathy as determined by digital infrared thermographic imaging

The diagnostic accuracy of infrared thermography in lumbosacral radicular pain: a prospective study

BACKGROUND

To identify the sensitivity, specificity, and overall diagnostic accuracy of infrared thermography in diagnosing lumbosacral radicular pain.

METHODS

Patients sequentially presenting with lower extremity pain were enrolled. A clinical certainty score ranging from 0 to 10 was used to assess the likelihood of lumbosacral radicular pain, with higher scores indicating higher likelihood. Infrared Thermography scans were performed and the temperature difference (ΔT) was calculated as ΔT = T1 - T2, where T2 represents the skin temperature of the most painful area on the affected limb and T1 represents the skin temperature of the same area on the unaffected limb. Upon discharge from the hospital, two independent doctors diagnosed lumbosacral radicular pain based on intraoperative findings, surgical effectiveness, and medical records.

RESULTS

A total of 162 patients were included in the study, with the adjudicated golden standard diagnosis revealing that 101 (62%) patients had lumbosacral radicular pain, while the lower extremity pain in 61 patients was attributed to other diseases. The optimal diagnostic value for ΔT was identified to fall between 0.8℃ and 2.2℃, with a corresponding diagnostic accuracy, sensitivity, and specificity of 80%, 89%, and 66% respectively. The diagnostic accuracy, sensitivity, and specificity for the clinical certainty score were reported as 69%, 62%, and 79% respectively. Combining the clinical certainty score with ΔT yielded a diagnostic accuracy, sensitivity, and specificity of 84%, 77%, and 88% respectively.

CONCLUSION

Infrared thermography proves to be a highly sensitive tool for diagnosing lumbosacral radicular pain. It offers additional diagnostic value in cases where general clinical evaluation may not provide conclusive results.

TRIAL REGISTRATION

ChiCTR2300078786, 19/22/2023.

Reference Standards for Digital Infrared Thermography Measuring Surface Temperature of the Upper Limbs

(1) Background: although digital infrared thermographic imaging (DITI) is used for diverse medical conditions of the upper limbs, no reference standards have been established. This study aims to establish reference standards by analyzing DITI results of the upper limbs. (2) Methods: we recruited 905 healthy Korean adults and conducted thermography on six regions (dorsal arm, ventral arm, lateral arm, medial arm, dorsal hand, and ventral hand region). We analyzed the data based on the proximity of regions of interest (ROIs), sex, and age. (3) Results: the average temperature (°C) and temperature discrepancy between the right and the left sides (ΔT) of each ROI varied significantly (p < 0.001), ranging from 28.45 ± 5.71 to 29.74 ± 5.14 and from 0.01 ± 0.49 to 0.15 ± 0.62, respectively. The temperature decreased towards the distal ROIs compared to proximal ROIs. The average temperatures of the same ROIs were significantly higher for men than women in all regions (p < 0.001). Across all regions, except the dorsal hand region, average temperatures tended to increase with age, particularly in individuals in their 30s and older (p < 0.001). (4) Conclusions: these data could be used as DITI reference standards to identify skin temperature abnormalities of the upper limbs. However, it is important to consider various confounding factors, and further research is required to validate the accuracy of our results under pathological conditions.

Reference Standard for Digital Infrared Thermography of the Surface Temperature of the Lower Limbs

Digital infrared thermographic imaging (DITI) is a supplementary diagnostic technique to visualize the surface temperature of the human body. However, there is currently no reference standard for the lower limbs for accurate diagnosis. In this study, we performed DITI on the lower limbs of 905 healthy Korean volunteers (411 males and 494 females aged between 20 and 69 years) to obtain reference standard data. Thermography was conducted on the front, back, lateral sides, and sole area, and 188 regions of interest (ROIs) were analyzed. Additionally, subgroup analysis was conducted according to the proximity of ROIs, sex, and age groups. The mean temperatures of ROIs ranged from 24.60 ± 5.06 to 28.75 ± 5.76 °C and the absolute value of the temperature difference between both sides reached up to 1.06 ± 2.75 °C. According to subgroup analysis, the sole area had a significantly lower temperature than any other areas, men had higher temperatures than women, and the elderly had higher temperatures than the young adults except for the 20s age group (p < 0.001, respectively). This result could be used as a foundation for the establishment of a reference standard for DITI. Practical patient DITI can be accurately interpreted using these data, and it can serve as a basis for further scientific research.

Differential Screening of Herniated Lumbar Discs Based on Bag of Visual Words Image Classification Using Digital Infrared Thermographic Images

Doctors in primary hospitals can obtain the impression of lumbosacral radiculopathy with a physical exam and need to acquire medical images, such as an expensive MRI, for diagnosis. Then, doctors will perform a foraminal root block to the target root for pain control. However, there was insufficient screening medical image examination for precise L5 and S1 lumbosacral radiculopathy, which is most prevalent in the clinical field. Therefore, to perform differential screening of L5 and S1 lumbosacral radiculopathy, the authors applied digital infrared thermographic images (DITI) to the machine learning (ML) algorithm, which is the bag of visual words method. DITI dataset included data from the healthy population and radiculopathy patients with herniated lumbar discs (HLDs) L4/5 and L5/S1. A total of 842 patients were enrolled and the dataset was split into a 7:3 ratio as the training algorithm and test dataset to evaluate model performance. The average accuracy was 0.72 and 0.67, the average precision was 0.71 and 0.77, the average recall was 0.69 and 0.74, and the F1 score was 0.70 and 0.75 for the training and test datasets. Application of the bag of visual words algorithm to DITI classification will aid in the differential screening of lumbosacral radiculopathy and increase the therapeutic effect of primary pain interventions with economical cost.