The mean and maximum thermal asymmetry (Update)
A thermal asymmetry on a region of interest (ROI) could show a potential injury risk due to factors related to workload assimilation, biomechanics, tissue physiology and nerve dysfunction. According to our research and practical experience, we propose a thermal asymmetry classification scale, which will start from bilateral skin temperature differences above 0.3ºC.
Skin temperature regulation is a complex system that depends on blood-flow rate, local structures of subcutaneous tissues and the activity of the sympathetic nervous system. Based on the homeostasis principle, humans are supposed to be thermally balanced, that is to say, we should have similar temperatures in the left and right regions.
Qualitative vs. quantitative analysis
In order to evaluate the skin temperature in humans we can use infrared thermography with two different methodologies:
- The qualitative method, which gives us the possibility to examine the thermal image subjectively interpreting the colors. Any experienced thermographer can perform this kind of analysis immediately. It is fast and very intuitive, but may be risky, because it is based on the subjective interpretation of the technician that reads colors that might be modified easily using the scale, so it is quite easy to underestimate or overestimate a color spot.
- The quantitative method is based on the radiometric data contained within the pixels of the thermal image. This means that it allows us to carry out a reproducible, reliable and comparable analysis through software. When using the quantitative method, the main challenge that we face is the variability of the skin temperature due to the influence factors, which forces us not to focus on absolute temperatures (for instance: this knee is 28,5ºC). Among relative temperatures, thermal asymmetry is one of the most solid and used metrics nowadays.
During the quantitative method, the analysis results are shown throughout metrics. The asymmetries are some of the metrics we can use to better understand human physiology.
Why asymmetries in thermography?
From the ThermoHuman R&D group, we have recently analyzed 950 healthy athletes without any pain. The average asymmetry was 0.004ºC ± 0.066ºC, as shown in figure 1. That is, an almost perfect symmetry. In other words, the average athlete has a high level of homeostasis in all regions when healthy (Escamilla-Galindo et al. 2022).
Authors such as Uematsu (1988) have shown that in asymptomatic individuals “the degree of thermal asymmetry between opposite sides of the body (AT) is very small”, with values under 0.38ºC. The thermal differences between bilateral regions of interest (ROI), with maximum or average temperatures have been shown as a valid method in several studies (Formenti et al. 2018).
That is why we use thermal asymmetries from the first image. In ThermoHuman software, we created a classification scale highlighting with different colors of thermal asymmetries above 0.3ºC, so it is very intuitive to spot areas that are not in a thermal balance with a simple glance.
Mean and maximum asymmetry
Two of the most used metrics: mean and maximum asymmetry
- The mean asymmetry compares the average skin temperature of one ROI with the bilateral one.
- In the maximum asymmetry, it follows the same principle as the mean asymmetry, but comparing the maximum temperature pixel within the ROI with its contralateral.
As can be seen in figure 2, both metrics are validated and show similar results:
If you want to know the difference between these two metrics or learn more about other metrics used in ThermoHuman, click here.
The human body is designed to maintain a balance, known in biomedical sciences as homeostasis. Thermoregulation is one of the main systems ruled by this principle. If we want to evaluate the skin temperature in humans we can use infrared thermography with different methods, as well as several metrics. Metrics that use asymmetry are extremely useful to have a deeper understanding on the physiology of the athletes and patients.
Thermography applications have been discussed in other posts to improve understanding of the tool.
Fernández-Cuevas, Ismael, et al. “Infrared thermography for the detection of injury in sports medicine.” Application of infrared thermography in sports science. Springer, Cham, 2017. 81-109.
Formenti, D., Ludwig, N., Rossi, A., Trecroci, A., Alberti, G., Gargano, M., . . . Caumo, A. (2018). Is the maximum value in the region of interest a reliable indicator of skin temperature? Infrared Physics & Technology, 94, 299-304.
Uematsu, S., Edwin, D. H., Jankel, W. R., Kozikowski, J., & Trattner, M. (1988). Quantification of thermal asymmetry. Part 1: Normal values and reproducibility. J Neurosurg, 69(4), 552-555.
If you have any questions or would like to make a comment, do not hesitate to write to us. We will be happy to read you.