Aim To evaluate the potential of thermography as an assessment tool for the detection of foot complications by understanding the variations in temperature that occur in type 2 diabetes mellitus (DM). Methods Participants were categorized according to a medical examination, ankle brachial index, doppler waveform analysis, and 10-gram monofilament testing into five groups: healthy adult, DM with no complications, DM with peripheral neuropathy, DM with neuroischaemia, and DM with peripheral arterial disease (PAD) groups. Thermographic imaging of the toes and forefeet was performed. Results 43 neuroischaemic feet, 41 neuropathic feet, 58 PAD feet, 21 DM feet without complications, and 126 healthy feet were analyzed. The temperatures of the feet and toes were significantly higher in the complications group when compared to the healthy adult and DM healthy groups. The higher the temperatures of the foot in DM, the higher the probability that it is affected by neuropathy, neuroischaemia, or PAD. Conclusions Significant differences in mean temperatures exist between participants who were healthy and those with DM with no known complications when compared to participants with neuroischaemia, neuropathy, or PAD. As foot temperature rises, so does the probability of the presence of complications of neuropathy, neuroischaemia, or peripheral arterial disease.
Contrary to expectations the mean toe and forefoot temperatures in DM patients with PAD is higher than in those with DM only. This unexpected result could be attributed to disruption of noradrenergic vasoconstrictor thermoregulatory mechanisms with resulting increased flow through cutaneous vessels and subsequent increased heat emissivity. These results demonstrate that thermography may have potential in detecting PAD and associated temperature differences.
Type 2 diabetes mellitus (T2DM) is a medical condition that has reached pandemic proportions, causing a worldwide health crisis according to the International Diabetes Federation. It has long been established that this condition may give rise to several serious complications in the foot, including neuropathy and peripheral arterial disease, which may also coexist as neuroischaemia. 1 The latter are very vulnerable to injury, both from minor and major trauma, and may develop ulcerations, or even amputations. 2 Accordingly, the risk of amputations is very high, 2-4 calling for early identification of at-risk feet. 2,5,6 The need to improve foot screening to improve outcomes remains. In this context, medical infrared imaging, or thermography, has been widely recognized as a noninvasive, potential tool for detecting temperature difference in patients with diabetes in order to detect vascular and neuropathic changes. 7 Normal thermographic patterns have been established for healthy, normal diabetes mellitus, neuropathic, and neuroischemic feet. 8,9 However, we lack data on the utility of thermography in the assessment of neuroischemic ulcers. Should the presence of ulceration provide a distinctive thermal image pattern, this might prove useful as a 783910I JLXXX10.
This work develops a method for automatically extracting temperature data from prespecified anatomical regions of interest from thermal images of human hands, feet, and shins for the monitoring of peripheral arterial disease in diabetic patients. Binarisation, morphological operations, and geometric transformations are applied in cascade to automatically extract the required data from 44 predefined regions of interest. The implemented algorithms for region extraction were tested on data from 395 participants. A correct extraction in around 90% of the images was achieved. The process of automatically extracting 44 regions of interest was performed in a total computation time of approximately 1 minute, a substantial improvement over 10 minutes it took for a corresponding manual extraction of the regions by a trained individual. Interrater reliability tests showed that the automatically extracted ROIs are similar to those extracted by humans with minimal temperature difference. This set of algorithms provides a sufficiently accurate and reliable method for temperature extraction from thermal images at par with human raters with a tenfold reduction in time requirement. The automated process may replace the manual human extraction, leading to a faster process, making it feasible to carry out large-scale studies and to increase the regions of interest with minimal cost. The code for the developed algorithms, to extract the 44 ROIs from thermal images of hands, feet, and shins, has been made available online in the form of MATLAB functions and can be accessed from http://www.um.edu.mt/cbc/tipmid.
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