How can I use ThermoHuman metrics?
- INTRODUCTION TO METRICS
- THERMAL ASYMMETRY
- AVERAGE THERMAL ASSYMETRY
- MAXIMUM THERMAL ASYMMETRY
- NEUTRALIZED ASYMMETRY
- COEFFICIENT OF VARIATION
- SOFTENED COEFFICIENT OF VARIATION
- THE MAXIMUM TEMPERATURE OF THE FACE
- THERMOGRAMS WITH CONSTANT COLOR SCALE
- ALARM SYSTEM
INTRODUCTION TO METRICS
If we want to evaluate the skin temperature in humans we can use infrared thermography with different methods, as well as several metrics.
An experimented thermographer might be able to perform an immediate analysis using the qualitative method, which gives us the possibility to examine the thermal image interpreting the colours. It is fast and very intuitive, but risky, because it is based on the subjective interpretation of the technician that reads colours that might be modified easily using the scale, so quite easy to underestimate or overestimate a colour spot.
On the other hand, we have the quantitative method, based on the radiometric data contained within the pixels of the thermal image, allowing us to carry out a reproducible, reliable and comparable analysis through a 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.
Thermography applications have been discussed in other posts to improve understanding of the tool.
The human body is designed to maintain a balance, in bio-medical sciences this concept is known as homeostasis. Thermoregulation is one of the main systems ruled by this principle. That is why, authors as Uematsu (1988) has showed in asymptomatic normal individuals that “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 colours thermal asymmetries above 0.3ºC, so it is very intuitive to spot areas that are not in a thermal balance with a simple glance.
AVERAGE THERMAL ASSYMETRY
Thermal asymmetry (showed as Asymmetry in ThermoHuman, figure 1) compares the average skin temperature of one ROI with the bilateral one. We recommend using these metrics mainly if you are analyzing a subject for the first time or in the following evaluations. In this example, we can observe the first evaluation of an athlete, who practices a collective sport that involves high-intensity movements. ThermoHuman avatars show us significant asymmetries (above 0.3ºC) on the left ankle and foot, and on the posterior right thigh area that might be produced by that demand of repeated efforts of his sport.
In a recent investigation, from the ThermoHuman group, it has been seen that analyzing 950 healthy athletes without pain, the general difference in the asymmetry metrics is 0.004ºC ±0.066ºC (see figure 2). That is, a high level of homeostasis is expected in all regions when the athlete is healthy (Escamilla-Galindo et al. 2022).
The following video shows the oral communication presented at the ECSS 2022 with the investigation of normal temperature values between regions in 950 athletes:
MAXIMUM THERMAL ASYMMETRY
The analysis of the maximum thermal asymmetries (showed as Maximum Asymmetry in ThermoHuman) is an interesting alternative. It follows the same principle as average thermal asymmetry, but comparing the maximum temperature data within the ROI with its bilateral area. Both metrics (maximum and average thermal asymmetries) are validated and show similar results.
This metric is especially useful when we find pathologies that generate a localized and significant increase of skin temperature (what we know as a hot spot) but that does not affect the entire ROI and might not create an alarm using average thermal asymmetry. In the next example, we see the case of a patient affected by human papilloma virus (visible on figure 4, on the right first metatarsal joint). On the left (figure 3), we observe no significant average thermal asymmetry on that ROI, while if we analyse it with the maximum asymmetry we obtain a very relevant alarm as we see in right avatar.
“Not all asymmetries mean injury”, this is one of the quotes that we use the most. Neutralized asymmetry is one of the metrics that helps us to identify better if a thermal asymmetry is relevant or might not.
Although Uematsu (1988) and other authors showed, in some cases, we find thermal asymmetries that are above 0.3ºC (even much more) on areas with no pain, previous injury or affected by influence factors. With just one image, it is hard to differentiate, but if we have the chance of assessing someone frequently we might observe consistent and repeated alarms in some ROIs.
This metric requires several evaluations to establish, as long as there is no pain or injury, a localized an individual threshold based on the historical average of each ROI. That is what we know as building an individualized thermal profile. An example of the usefulness of this metric is to neutralize the asymmetries generated by an asymmetric sport such as tennis or judo, where the forearm of the grip will present a hyperthermic asymmetry produced by adaptation to the sport itself (Arnaiz-Lastras et al. 2011), as we can see on figure 5:
In the following example, we see the tracking report of a subject with a consistent asymmetry alarm on his left knee (figure 6). Since he is pain-free and uninjured, when we choose the neutralized asymmetry option (figure 7), the alarm on the knee just appears during the sixth evaluation, when it is actually significant.
COEFFICIENT OF VARIATION
Using only thermal asymmetries (regardless of it is average, maximum or neutralized asymmetry) has two main limitations: firstly, we are not able to detect bilateral issues; and secondly, we mainly focus on the warmer area, but we do not know if it is actually because this ROI is getting warmer or the bilateral is getting colder.
Coefficient of variation is the perfect metrics to solve those issues and complement the use of thermal asymmetries. As it also happens with neutralized asymmetry, the coefficient of variation requires several thermograms to build a consistent thermal profile over the time. It basically allows us to analyse the thermal tendency of one ROI regardless of the bilateral ROI and the absolute temperature, it is a metric based on the historical average temperature an standard deviation of assessments done without pain, injury or influence factors.
In the following example we can observe the first tracking report showing the average asymmetry avatars from a soccer player (figure 8). It is remarkable the thermal asymmetry on the left knee. When we use the coefficient of variation (figure 9) we can clearly see on the second evaluation that both ankles and knees were getting warmer (regardless of the asymmetry). On the fourth evaluation, the left knee was warmer than the right one (that is what the asymmetry is telling us) and the coefficient of variation complements it showing a hypothermic tendency, that is to say: the left knee is warmer than the right one but getting colder.
This is key factor to understand the thermoregulation behaviour of the different tissues, because the coefficient of variation allows us to understand over the time, if a certain asymmetry is the result of one ROI increasing temperature, the opposite decreasing or even both tendencies as the same time. A very practical example is what happens when a muscle injury occurs: it normally creates an asymmetry, but on the opposite region. Why? Because in case of significant asymmetries, ThermoHuman always highlights the warmer ROI, but it does not mean that the problem is rigth there (take a look of our publication about the importance of hyperthermia an hypothermia). Coefficient of variation might help you to understand that what was happening is that the injured ROI is getting colder, and not the opposite. We can see that on the following right calf muscle injury case, firstly with neutralized asymmetry (figure 10), then with coefficient of variation (figure 11). The injury ROI is outlined in purple because it is also painful.
SOFTENED COEFFICIENT OF VARIATION
In some cases, the result of the coefficient of variation can show extreme tendencies. As you can observe in the following tracking report on figure 12, the coefficient of variation outcomes show extreme cold and warm tendencies from one evaluation to the following one.
This can be the case in subjects that have been analyzed over the time with long periods between evaluations or with different external conditions (warmer or colder ambient temperatures) creating global changes on the whole body (as we can see in the example above). To avoid this bias, we use the softened coefficient of variation, which subtracts the general increase or decrease in skin temperatures to emphasize those ROI that have undergone a significant change.
THE MAXIMUM TEMPERATURE OF THE FACE
In order to display the maximum temperature of the face it is necessary to carry out an upper body protocol from the front (AP protocol). Remember not to make any of these common evaluation mistakes so that the photo can be processed. Automatically, the software will segment and calculate the temperature of the face within that protocol and display it in the “hoover“, that is, the last of the data on the poster that appears when placing the mouse over a region of interest in the avatar view (see figure 13):
As can be seen in Figure 13, the temperature of the face can be displayed by placing the cursor over any ROI, in this case the ROI of the chest was chosen, and in the last line the temperature of the face appears with a value of 35.2 ºC.
This data allows us to visualize a region of special interest due to its relationship with the body’s core temperature, its stability in repeated evaluations and the ability to generate proportions with other regions. We recall that the maximum temperature of the face is related to febrile processes and allows us to relate the internal temperature with the temperature of the inner corner of the eye through these metrics (Zhou et al. 2020; Pascoe et al. 2010; Mercer et al. 2009).
In Figure 14 we can see the region of the face where temperature is measured in feverish states. The difference between core temperature (Tcore) and skin temperature (Tskin) is considered to be ±0.5 ºC, so if the measurement exceeds 39 ºC, it is considered dangerous.
Isotherms are a common functionality in the field of thermography and even thermal imaging cameras include a function that allows the most opposite ranges of the scale to be displayed. At ThermoHuman we have included this possibility from the selection of avatars.
It is possible to select in the group report or in the individual follow-up report the isotherm functionality to display, on a scale of 1 Z score, 1.5 Z score and 2 score Z metrics above and below the standard deviation, the most opposite values of the scale (see figure 15):
With this functionality, abnormal patterns that are related to hyperthermic and/or hypothermic areas are quickly displayed. In addition, this visualization allows to see those individual patterns that are repeated over time as a result of the athletes’ condition and that allow a more advanced analysis (Barcelos et al. 2014). It is an advanced scale of thermograms that highlights the most hypothermic and the most hyperthermic.
THERMOGRAMS WITH CONSTANT COLOR SCALE
Another functionality that ThermoHuman has is the ability to modify the scale within the software itself to establish a more adapted vision to the needs. This scale change affects all selected protocols in both group and follow-up reports. This fact is noteworthy, since once the scale is modified and the modification is accepted, the changes, being modifiable again, will not be reversible at the first visualization.
ThermoHuman, at first when it segments and analyzes, generates an image with a color scale based on the maximum and minimum values of the image itself that allows optimization of the display range, maximizing the contrast between the highest and lowest values. temperature drops. By optimizing the range of each image, the scales are different in the historical series of a person or a group.
For this reason, this way of viewing does not allow a comparison between the images analyzed at different times, because in two different images the same color does not represent the same temperature value.
Hence the need to create thermograms with a constant color scale to allow viewing all thermograms with the same scale defined by the user, so that in these compared views the thermal evolution can be easily seen, at the expense of being able to lose contrast within the same image.
When we get into a group report or a follow-up report and access this functionality we can see that when we select a protocol and decide to make a scale change, it is applied to all the images with that protocol selected (see figure 16):
This modification allows us to quickly observe on which days there has been a qualitative change in temperature towards both hot and cold regions, since it homogenizes the temperatures of the scale for all the images of the different days. In addition, it is especially useful in group reports, since when viewing the different individuals, it is qualitatively appreciated who is colder and/or hotter in the evaluation of the same day. Finally, it is an advanced analysis that allows you to find hyperthermic and/or hypothermic regions and foci in a similar way to isotherms.
For example, at a qualitative level it seems that the player on January 31 and February 8 was generally colder, while on February 3 she was warmer.
The latest functionality within the software is the inclusion of the alarm system. Under an advanced calculation, with an equation that takes into account seven variables (from the player’s injury history, the asymmetry of the region to the epidemiology of the sport), a series of traffic light alarms are calculated (yellow, orange and red) and are placed as labels in the tooltip, the area above the evaluations. When selecting the alarm view, the legend on the side informs us of the meaning of each color (see image 17):
Table 1 shows the explanation for each value of the alarm system scale:
If the software detects that the player has an abnormality in one of the regions that may pose a risk of injury, a flag will appear for that region based on the appropriate equation. Let us remember that the software learns from the data that we provide, therefore if we add the previous pain regions or previous injuries, the alarm metrics will have a greater statistical power. Similarly, we need to feed the influencer data to the software. If we do not do it correctly, it can be the case of having a serious alarm in a case in which there is a bad segmentation, an ice application has been carried out, treatment… that generates a significant asymmetry).
This alarm system metrics allows us to screen the regions with the greatest relevance in an individual within the individual report, follow-up or in the group reports, to filter more quickly those evaluations that are of greater importance (see figure 18):
As we can see in the example of figure 19, the most significant day is February 3, in which red and orange labels appear that indicate that the right knee and leg in general have an abnormal thermal pattern and that there would be to choose an intervention strategy to solve it. Here you have an example:
The logic of the alarm system is to offer a first level of approximation to improve the interpretation of the ThermoHuman software and that these labels serve to offer different levels of intervention depending on the urgency and the level of alert shown by the alarm system to the staff of the team.
All metrics requiring several evaluations, thus using historical averages (neutralized asymmetry and both coefficient of variations) might show you this alarm on the tracking report:
It means that the values of this session are not used in the historical metrics calculation because it has been indicated that some ROI has pain, injury and/or the presence of influence factors. Therefore you might not have an avatar or the results are based on calculations of other “clean” sessions.
If you want to include those session in the global calculation just click on the following funnel button:
So you will be able to see those metrics including sessions with pain, injury an/or influence factors on figure 20.
In conclusion, thermography allows us to use metrics from the first evaluation. In this sense:
- One or few assessments: Use mainly average thermal asymmetry (asymmetry as it is shown in ThermoHuman) to spot significant imbalances, useful for prevention and diagnosis support
- More than 4 evaluations: Mainly in cases where the focus is prevention, we highly recommend you to start using neutralized asymmetry since it will be more accurate than the single average thermal asymmetry to spot the significant imbalances, in that case with alarms based on the historical thermal profile of this subject.
- More than 4 evaluations: In all application approaches (prevention, diagnosis support, injury monitoring or internal load quantification) both coefficient of variations will help you to understand if, regardless of the existence of asymmetries, there are significant warmer or colder thermal tendencies.
Infrared thermography is a solution that allows us to get relevant information from the first moment. Obviously, the more evaluations we have the better, because besides ThermoHuman will give us the possibility of richer and complementary analysis using all these metrics.
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.
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.
Arnaiz Lastras, J., Fernández Cuevas, I., Gómez Carmona, P. M., Sillero Quintana, M., García de la Concepción, M. Á., & Piñonosa Cano, S. (2011, 6th-9th july). Pilot study to determinate thermal asymmetries in judokas. Paper presented at the 16th Annual Congress of the European College of Sport Sciences ECSS, Liverpool, United Kingdom.
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.