A quantitative index for classification of plantar thermal changes in the diabetic foot

Hernandez-Contreras, D.; Peregrina-Barreto, Hayde; Rangel-Magdaleno, José; Gonzalez, Jesús A.; Altamirano, Leopoldo

doi:10.1016/j.infrared.2017.01.010

Cited by 42 publications

(49 citation statements)

References 21 publications

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“…Previous research of some authors in this work showed that it is possible to estimate local plantar temperatures based on the angiosome concept [19]. An angiosome is a composite unit of tissues supplied by an artery, providing valuable information relating temperature data to artery damage.…”

Section: Multiple Classesmentioning

confidence: 92%

“…An alternative to this approach is to measure the changes by computing a representative value for each foot of the DM group and taking the butterfly pattern as reference. Therefore, the measurement depends on the temperature distribution, and not on a spatial pattern [18][19][20]. This kind of analysis can help to describe thermograms to improve their automatic classification and bring additional information to the medical expert.…”

Section: Introductionmentioning

confidence: 99%

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Deep Learning Classification for Diabetic Foot Thermograms

Cruz-Vega

Hernandez-Contreras

Peregrina-Barreto

et al. 2020

Sensors

100

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According to the World Health Organization (WHO), Diabetes Mellitus (DM) is one of the most prevalent diseases in the world. It is also associated with a high mortality index. Diabetic foot is one of its main complications, and it comprises the development of plantar ulcers that could result in an amputation. Several works report that thermography is useful to detect changes in the plantar temperature, which could give rise to a higher risk of ulceration. However, the plantar temperature distribution does not follow a particular pattern in diabetic patients, thereby making it difficult to measure the changes. Thus, there is an interest in improving the success of the analysis and classification methods that help to detect abnormal changes in the plantar temperature. All this leads to the use of computer-aided systems, such as those involved in artificial intelligence (AI), which operate with highly complex data structures. This paper compares machine learning-based techniques with Deep Learning (DL) structures. We tested common structures in the mode of transfer learning, including AlexNet and GoogleNet. Moreover, we designed a new DL-structure, which is trained from scratch and is able to reach higher values in terms of accuracy and other quality measures. The main goal of this work is to analyze the use of AI and DL for the classification of diabetic foot thermograms, highlighting their advantages and limitations. To the best of our knowledge, this is the first proposal of DL networks applied to the classification of diabetic foot thermograms. The experiments are conducted over thermograms of DM and control groups. After that, a multi-level classification is performed based on a previously reported thermal change index. The high accuracy obtained shows the usefulness of AI and DL as auxiliary tools to aid during the medical diagnosis.

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Section: Multiple Classesmentioning

confidence: 92%

Section: Introductionmentioning

confidence: 99%

Deep Learning Classification for Diabetic Foot Thermograms

Cruz-Vega

Hernandez-Contreras

Peregrina-Barreto

et al. 2020

Sensors

100

View full text Add to dashboard Cite

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“…They concluded that the estimated temperature difference did not show any difference useful for analysis and that the HSE (hot spot estimator) was capable of detecting hot spots which would turn out to be ulcer later which the ETD (estimated temperature difference) was not able to. Hernandez-Contreras et al [21] were able to observe the butterfly pattern associated with nondiabetic subjects. They formulated an index called the TCI (thermal change index) based on the temperature readings of the corresponding angiosomes of diabetic and nondiabetic subjects.…”

Section: Literature Review On Diabetic Foot Complications Assessment mentioning

confidence: 97%

Infrared Thermography and Soft Computing for Diabetic Foot Assessment

Gururajarao

Venkatappa

Shivaram

et al. 2019

Machine Learning in Bio-Signal Analysis and Diagnostic Imaging

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“…In the last decade, numerous methodologies or methods have been introduced in the literature for identifying several medical conditions such as diabetes [1,2], glaucoma [3,4], breast cancer [5,6], emotion detection [7], among others. Also, some proposals have been focused on the detection of several neurological disorders such as Alzheimer's disease (AD) [8][9][10][11], mild cognitive impairment (MCI) [12][13][14][15], delirium [16], epilepsy [17][18][19], sleep disorder [20][21][22], Parkinson's disease [23,24], autism spectrum disorder [25], schizophrenia [26,27], among others.…”

Section: Introductionmentioning

confidence: 99%

A New Methodology Based on EMD and Nonlinear Measurements for Sudden Cardiac Death Detection

Vargas-Lopez

Amezquita-Sanchez

De-Santiago-Perez

et al. 2019

Sensors

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Heart diseases are among the most common death causes in the population. Particularly, sudden cardiac death (SCD) is the cause of 10% of the deaths around the world. For this reason, it is necessary to develop new methodologies that can predict this event in the earliest possible stage. This work presents a novel methodology to predict when a person can develop an SCD episode before it occurs. It is based on the adroit combination of the empirical mode decomposition, nonlinear measurements, such as the Higuchi fractal and permutation entropy, and a neural network. The obtained results show that the proposed methodology is capable of detecting an SCD episode 25 min before it appears with a 94% accuracy. The main benefits of the proposal are: (1) an improved detection time of 25% compared with previously published works, (2) moderate computational complexity since only two features are used, and (3) it uses the raw ECG without any preprocessing stage, unlike recent previous works.

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A quantitative index for classification of plantar thermal changes in the diabetic foot

Cited by 42 publications

References 21 publications

Deep Learning Classification for Diabetic Foot Thermograms

Deep Learning Classification for Diabetic Foot Thermograms

Infrared Thermography and Soft Computing for Diabetic Foot Assessment

A New Methodology Based on EMD and Nonlinear Measurements for Sudden Cardiac Death Detection

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