Morphological Foot Model for Temperature Pattern Analysis Proposed for Diabetic Foot Disorders

Arteaga-Marrero, Natalia; Bodson, Lucas Christian; Hernández, Abián; Villa, Enrique; Ruiz-Alzola, Juan

doi:10.3390/app11167396

Cited by 7 publications

(10 citation statements)

References 43 publications

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“…Our search highlighted articles that had analyzed and compared foot temperature using infrared thermography in healthy [ 11 , 12 , 13 , 14 ] and diabetic patients [ 3 , 5 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 ], although other studies had directly analyzed both types of patients [ 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 , 34 ]. This type of comparison had confirmed the difference between the temperature of both groups, registering lower temperatures in the healthy group as compared with the diabetic one [ 3 , 5 , 24 , 25 , 26 , 27 , 32 , 33 ]. However, it is important to consider the results obtained depend on the protocol used.…”

Section: Discussionmentioning

confidence: 99%

“…Furthermore, Mori et al [ 23 ] suggest that temperature distribution patterns must be identified by collecting and classifying these images according to maps of heat distribution so that they can be compared without relying on the angiosome divisions proposed by other authors [ 5 , 23 ]. Other authors suggest that patients with diabetic foot present a significantly higher temperature in the fifth metatarsal area and hallux compared to healthy individuals [ 20 , 22 , 27 ]. These variations represent different pattern distributions of temperature.…”

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

Update on the Use of Infrared Thermography in the Early Detection of Diabetic Foot Complications: A Bibliographic Review

Faus Camarena,

Izquierdo-Renau,

Julian-Rochina

et al. 2023

Sensors

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Foot lesions are among the most frequent causes of morbidity and disability in the diabetic population. Thus, the exploration of preventive control measures is vital for detecting early signs and symptoms of this disease. Infrared thermography is one of the complementary diagnostic tools available that has proven to be effective in the control of diabetic foot. The last review on this topic was published in 2015 and so, we conducted a bibliographic review of the main databases (PubMed, the Web of Science, Cochrane library, and Scopus) during the third quarter of 2023. We aimed to identify the effectiveness of infrared thermography as a diagnostic element in pre-ulcerous states in diabetic patients and to detect diabetic foot ulcer complications. We obtained a total of 1999 articles, 26 of which were finally included in the present review and published after 2013. After analyzing the use of infrared thermography in diabetic patients both with and without ulcers, as well as in healthy individuals, we concluded that is an effective tool for detecting early-stage ulcers in diabetic foot patients.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Update on the Use of Infrared Thermography in the Early Detection of Diabetic Foot Complications: A Bibliographic Review

Faus Camarena,

Izquierdo-Renau,

Julian-Rochina

et al. 2023

Sensors

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“…Infrared (IR) images were acquired using an affordable TE-Q1 Plus camera from Thermal Expert™ (i3system Inc., Daejeon, Republic of Korea), described in detail in a previous publication [ 45 ]. An acquisition campaign was carried out among healthy coworkers (N = 22) at IACTEC, composed of nine women and thirteen men [ 46 ]. For each subject, four images were acquired at T0, T5, T10, and T15.…”

Section: Methodsmentioning

confidence: 99%

Feature Ranking by Variational Dropout for Classification Using Thermograms from Diabetic Foot Ulcers

Hernandez-Guedes

Arteaga-Marrero

Villa

et al. 2023

Sensors

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Diabetes mellitus presents a high prevalence around the world. A common and long-term derived complication is diabetic foot ulcers (DFUs), which have a global prevalence of roughly 6.3%, and a lifetime incidence of up to 34%. Infrared thermograms, covering the entire plantar aspect of both feet, can be employed to monitor the risk of developing a foot ulcer, because diabetic patients exhibit an abnormal pattern that may indicate a foot disorder. In this study, the publicly available INAOE dataset composed of thermogram images of healthy and diabetic subjects was employed to extract relevant features aiming to establish a set of state-of-the-art features that efficiently classify DFU. This database was extended and balanced by fusing it with private local thermograms from healthy volunteers and generating synthetic data via synthetic minority oversampling technique (SMOTE). State-of-the-art features were extracted using two classical approaches, LASSO and random forest, as well as two variational deep learning (DL)-based ones: concrete and variational dropout. Then, the most relevant features were detected and ranked. Subsequently, the extracted features were employed to classify subjects at risk of developing an ulcer using as reference a support vector machine (SVM) classifier with a fixed hyperparameter configuration to evaluate the robustness of the selected features. The new set of features extracted considerably differed from those currently considered state-of-the-art but provided a fair performance. Among the implemented extraction approaches, the variational DL ones, particularly the concrete dropout, performed the best, reporting an F1 score of 90% using the aforementioned SVM classifier. In comparison with features previously considered as the state-of-the-art, approximately 15% better performance was achieved for classification.

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“…The database is expected to provide a valuable source to promote research about the potential of infrared thermography for the early diagnosis of diabetic foot problems. Further, The INAOE dataset has been enhanced by the inclusion of a local dataset acquired in 2021, which comprises images from 22 healthy volunteers recorded at four distinct time points, specifically utilizing those captured after a 15-minute resting period ( 71 , 73 ). This local dataset was merged with the INAOE dataset to create an extended collection aimed at balancing the previously noted skew towards diabetic cases.…”

Section: Datasetsmentioning

confidence: 99%

The role of machine learning in advancing diabetic foot: a review

Guan,

Wang,

Niu

et al. 2024

Front. Endocrinol.

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BackgroundDiabetic foot complications impose a significant strain on healthcare systems worldwide, acting as a principal cause of morbidity and mortality in individuals with diabetes mellitus. While traditional methods in diagnosing and treating these conditions have faced limitations, the emergence of Machine Learning (ML) technologies heralds a new era, offering the promise of revolutionizing diabetic foot care through enhanced precision and tailored treatment strategies.ObjectiveThis review aims to explore the transformative impact of ML on managing diabetic foot complications, highlighting its potential to advance diagnostic accuracy and therapeutic approaches by leveraging developments in medical imaging, biomarker detection, and clinical biomechanics.MethodsA meticulous literature search was executed across PubMed, Scopus, and Google Scholar databases to identify pertinent articles published up to March 2024. The search strategy was carefully crafted, employing a combination of keywords such as “Machine Learning,” “Diabetic Foot,” “Diabetic Foot Ulcers,” “Diabetic Foot Care,” “Artificial Intelligence,” and “Predictive Modeling.” This review offers an in-depth analysis of the foundational principles and algorithms that constitute ML, placing a special emphasis on their relevance to the medical sciences, particularly within the specialized domain of diabetic foot pathology. Through the incorporation of illustrative case studies and schematic diagrams, the review endeavors to elucidate the intricate computational methodologies involved.ResultsML has proven to be invaluable in deriving critical insights from complex datasets, enhancing both the diagnostic precision and therapeutic planning for diabetic foot management. This review highlights the efficacy of ML in clinical decision-making, underscored by comparative analyses of ML algorithms in prognostic assessments and diagnostic applications within diabetic foot care.ConclusionThe review culminates in a prospective assessment of the trajectory of ML applications in the realm of diabetic foot care. We believe that despite challenges such as computational limitations and ethical considerations, ML remains at the forefront of revolutionizing treatment paradigms for the management of diabetic foot complications that are globally applicable and precision-oriented. This technological evolution heralds unprecedented possibilities for treatment and opportunities for enhancing patient care.

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Morphological Foot Model for Temperature Pattern Analysis Proposed for Diabetic Foot Disorders

Cited by 7 publications

References 43 publications

Update on the Use of Infrared Thermography in the Early Detection of Diabetic Foot Complications: A Bibliographic Review

Update on the Use of Infrared Thermography in the Early Detection of Diabetic Foot Complications: A Bibliographic Review

Feature Ranking by Variational Dropout for Classification Using Thermograms from Diabetic Foot Ulcers

The role of machine learning in advancing diabetic foot: a review

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