Detection of diabetic peripheral neuropathy using spatial-temporal analysis in infrared videos

Solíz, Peter; Agurto, Carla; Edwards, Ana; Jarry, Zyden; Simon, Janet E.; Calder, Christopher; Burge, Mark R.

doi:10.1109/acssc.2016.7869038

Cited by 4 publications

(4 citation statements)

References 21 publications

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“…The results for this analysis are displayed in figure 8. ATR results displayed in figure 8 show a better defined difference between patients and controls, which is an expected result because a high value of asymmetry and a low value of response and recovery after a thermal stimulus are signs of the deterioration in the metabolism of lower limbs, and characteristics of individuals with DM2 [8,9,24,21,25,10,22,23,27,4]. Therefore, by relating these values, a significant difference is obtained between control volunteers and patients.…”

Section: Results and Interpretationmentioning

confidence: 68%

“…In this work we propose to reduce the number of images acquired in this sequence at 2 key times during a thermally-induced response, reducing in that way the image acquisition time, and still delivering an acceptable diagnosis. This study of the thermal response is based on the concept of self-regulation, that is a basic function of the nervous system to an external stimulus [21][22][23][24][25].…”

Section: Image Acquisition and Data Analysismentioning

confidence: 99%

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Analysis of the density and distribution of entropy in biomedical infrared imaging for Diabetes Mellitus Type II

Ortiz-Sosa¹,

Fuentes-Oliver²,

García‐Segundo³

et al. 2021

Biomed. Phys. Eng. Express

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We report a methodology to analyze data extracted from infrared images. These pictures show the lower limbs of a cohort of individuals belonging to, (1) voluntary controls and (2) patients diagnosed with diabetes mellitus type II. The analysis is presented in terms of Cross Entropy and temperature distributions; both using the associated thermal histograms. The temperature analysis is placed in terms of comparing the extreme values of dS/dQ, for controls and patients. In this analysis for the frontal view, the values of specificity and sensitivity calculated were 77.77% and 91.66%, respectively. For the back view, the specificity and sensitivity obtained were 88.8% and 83.3%, respectively. Instead of that, the cross-entropy analysis is placed in the modality of self-referencing. In this part of the study we obtained the coefficient of asymmetry and thermal response (ATR). The values of specificity and sensitivity for the ATR quotient in both cases were 83.3%. The results of both studies have a significant correlation with glucose (p < 0.01) and HbA1c (p < 0.01). It means that both approaches have statistical correspondence. By means of the Mann-Whitney U test, for independent samples, we get that the characteristic parameters we analyze can be differentiated among the populations of interest with a significance of p < 0.05. This suggests that both studies show consistency with the clinical diagnosis; exhibiting clear differences between control and patient groups.

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Section: Results and Interpretationmentioning

confidence: 68%

Section: Image Acquisition and Data Analysismentioning

confidence: 99%

Analysis of the density and distribution of entropy in biomedical infrared imaging for Diabetes Mellitus Type II

Ortiz-Sosa¹,

Fuentes-Oliver²,

García‐Segundo³

et al. 2021

Biomed. Phys. Eng. Express

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show abstract

“…Vision Quest [12,30] has developed a thermal optical imaging system capable of detecting early symptoms of diabetic peripheral neuropathy in the plantar foot, which accounts for about 25% of hospital stays among diabetes patients. The system works by recording the thermalized video of the patient's foot during recovery from cold provocation.…”

Section: B Infrared Thermography For Diabetes Diagnosismentioning

confidence: 99%

Infrared Imaging for Human Thermography and Breast Tumor Classification using Thermal Images

Farooq

Corcoran

2020

2020 31st Irish Signals and Systems Conference (ISSC)

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Human thermography is considered to be an integral medical diagnostic tool for detecting heat patterns and measuring quantitative temperature data of the human body. It can be used in conjunction with other medical diagnostic procedures for getting comprehensive medication results. In the proposed study we have highlighted the significance of Infrared Thermography (IRT) and the role of machine learning in thermal medical image analysis for human health monitoring and various disease diagnosis in preliminary stages. The first part of the proposed study provides comprehensive information about the application of IRT in the diagnosis of various diseases such as skin and breast cancer detection in preliminary stages, dry eye syndromes, and ocular issues, liver disease, diabetes diagnosis and last but not least the novel COVID-19 virus. Whereas in the second phase we have proposed an autonomous breast tumor classification system using thermal breast images by employing state of the art Convolution Neural Network (CNN). The system achieves the overall accuracy of 80% and recall rate of 83.33%.

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“…Based on the important literature working on DF with thermal sensor, it seems clear that thermal information are valuable for the practitioner, but absolute temperature don't seem to be enough for an accurate classification of the foot state, transient information are needed [3]. Therefore, some works [4], [5] investigated the temporal thermal signal following a thermal stimulus.…”

Section: Introductionmentioning

confidence: 99%

Perfusion and thermal conductivity parameter extraction, from thermal imaging, for the quantification of the diabetic foot disease

Serantoni¹,

Jourdan²,

Louche³

et al. 2020

Proceedings of the 2020 International Conference on Quantitative InfraRed Thermography

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Diabetes Mellitus (DM) is a huge public health-care chronic disease issue, with about 425 millions of patients worldwide in 2017. DM is a serious pathology, especially through its complications and their severity, as one, called diabetic foot, which can lead to a lower limb amputation. The aim of the present study is to propose a method, based on the use of infrared imaging, to classify the foot state thanks to a bio-heat model. The protocol is based on a global cooling of the feet, the full thermal recording of the skin rewarming and on some parameters extraction.

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Detection of diabetic peripheral neuropathy using spatial-temporal analysis in infrared videos

Cited by 4 publications

References 21 publications

Analysis of the density and distribution of entropy in biomedical infrared imaging for Diabetes Mellitus Type II

Analysis of the density and distribution of entropy in biomedical infrared imaging for Diabetes Mellitus Type II

Infrared Imaging for Human Thermography and Breast Tumor Classification using Thermal Images

Perfusion and thermal conductivity parameter extraction, from thermal imaging, for the quantification of the diabetic foot disease

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