Document
Mexican National Council of Humanities, Science and Technology; Cátedras CONACYT; Consejo Nacional de Humanidades, Ciencias y Tecnologías, Conahcyt, (CVU 662217); Consejo Nacional de Humanidades, Ciencias y Tecnologías, Conahcyt
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RDF description of Digital infrared thermography and machine learning for diabetic foot assessment: thermal patterns and classification - http://orbis.uaslp.mx/vivo/individual/scopus11923
Article
Journal of Diabetes and Metabolic Disorders
Information Content Entity
Digital infrared thermography and machine learning for diabetic foot assessment: thermal patterns and classification
Diabetes mellitus; Diabetic foot; Digital infrared thermography; Machine learning; Principal component analysis; Support vector machine hemoglobin A1c; adult; Article; body mass; clinical article; controlled study; correlation analysis; cross validation; diabetic foot; diagnostic accuracy; diagnostic test accuracy study; digital infrared thermography; female; foot temperature; human; infrared photography; machine learning; male; median foot sole temperature; median foot temperature; middle aged; non insulin dependent diabetes mellitus; predictive value; process optimization; receiver operating characteristic; sensitivity and specificity; support vector machine; temperature; thermodynamics
Objectives: Digital infrared thermography is a noninvasive tool used for assessing diseases, including the diabetic foot. This study aims to analyze thermal patterns of the foot sole in patients with type 2 diabetes mellitus using thermography and explore correlations with clinical variables. Additionally, a machine learning approach was developed for classification. Methods: A total of 23 diabetic patients and 27 age- and sex-matched controls were included. Thermograms of the plantar foot surface were acquired and segmented into regions of interest. Mean foot temperature and temperature change index were calculated from predefined regions of interest. Pearson’s correlation analysis was conducted for temperature measures, glycated hemoglobin, and body mass index. A two-layered cross-validation model using principal component analysis and support vector machines were employed for classification. Results: Significant positive correlations were found between mean foot temperature and glycated hemoglobin (ρ = 0.44, p = 0.0015), as well as between mean foot temperature and body mass index (ρ = 0.35, p = 0.013). Temperature change index did not show significant correlations with clinical variables. The machine learning model achieved high overall accuracy (90%25) and specificity (100%25) with a moderate sensitivity (78.3%25) for classifying diabetic and control groups based on thermal data. Conclusions: Thermography combined with machine learning shows potential for assessing diabetic foot complications. Correlations between mean foot temperature and clinical variables suggest foot temperature changes as potential indicators. The machine learning model demonstrates promising accuracy for classification, suitable for screening purposes. Further research is needed to understand underlying mechanisms and establish clinical utility in diagnosing and managing diabetic foot complications.
FLIR One Pro infrared camera, Teledyne, United States