Thermal Imaging of the Tongue Surface as a Predictive Method in the Diagnosis of Type 2 Diabetes Mellitus

Wziątek-Kuczmik, Daria; Świątkowski, Antoni; Cholewka, Armand; Mrowiec, Aleksandra; Niedzielska, Iwona; Stanek, Agata

doi:10.3390/s24082447

Cited by 3 publications

(1 citation statement)

References 23 publications

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“…There are a number of non-invasive techniques available to assess the microcirculation [1]. Capillaroscopy [2][3][4][5][6][7][8] and videocapillaroscopy [9][10][11], laser Doppler flowmetry [12][13][14], thermography [15,16], and transcutaneous oxygen measurements [17][18][19][20][21][22][23] are of recognized clinical value. Therefore, any emerging methods for detecting early signs of microcirculatory changes should be evaluated, and if plausible, the standard for use should be established.…”

Section: Introductionmentioning

confidence: 99%

The Impact of Disease Duration on Microcirculatory Dysfunction in Young Patients with Uncomplicated Type 1 Diabetes

Neubauer-Geryk,

Wielicka,

Hoffmann

et al. 2024

Biomedicines

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This study aimed to evaluate the earliest changes in the structure and function of the peripheral microcirculation using capillaroscopy and transcutaneous oxygen pressure measurement in children and adolescents with type 1 diabetes mellitus at baseline and during post-occlusive reactive hyperemia (PORH) in the function of diabetes duration. Sixty-seven patients with type 1 diabetes mellitus (T1D), aged 8 to 18 years, and twenty-eight age- and sex-matched healthy subjects were included in the analysis. Diabetic patients were divided into subgroups based on median disease duration. The subgroups differed in chronological age, lipid levels, and thyroid hormones. Capillaroscopy was performed twice: at baseline and then again after the PORH test. Transcutaneous oxygen pressure also was recorded under baseline conditions during and after the PORH test. Comparison of capillaroscopy and transcutaneous oxygen pressure parameters at rest and after the PORH showed no statistically significant difference between the subgroups. This remained true after adjusting for variables that differentiated the two subgroups. However, in the group of patients with long-standing diabetes, significant negative correlations were observed between the Coverage value after the PORH test and capillary reactivity with TcPO2_zero (biological zero). Significant positive correlations were also found between distance after the PORH test and TcPO2_zero. The results of our study indicate that in patients with a shorter duration of diabetes, the use of multiple tests provides a better characterization of the structure and function of microcirculation because the onset of dysfunction does not occur at the same time in all the tests.

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Section: Introductionmentioning

confidence: 99%

The Impact of Disease Duration on Microcirculatory Dysfunction in Young Patients with Uncomplicated Type 1 Diabetes

Neubauer-Geryk,

Wielicka,

Hoffmann

et al. 2024

Biomedicines

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Pioneering diabetes screening tool: machine learning driven optical vascular signal analysis

Kumar,

Jeya Prabha

et al. 2024

Biomed. Phys. Eng. Express

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The escalating prevalence of diabetes mellitus underscores the critical need for non-invasive screening tools capable of early disease detection. Present diagnostic techniques depend on invasive procedures, which highlights the need for advancement of non-invasive alternatives for initial disease detection. Machine learning in integration with the optical sensing technology can effectively analyze the signal patterns associated with diabetes. The objective of this research is to develop and evaluate a non-invasive optical-based method combined with machine learning algorithms for the classification of individuals into normal, prediabetic, and diabetic categories. A novel device was engineered to capture real-time optical vascular signals from participants representing the three glycemic states. The signals were then subjected to quality assessment and preprocessing to ensure data reliability. Subsequently, feature extraction was performed using time-domain analysis and wavelet scattering techniques to derive meaningful characteristics from the optical signals. The extracted features were subsequently employed to train and validate a suite of machine learning algorithms. An ensemble bagged trees classifier with wavelet scattering features and random forest classifier with time-domain features demonstrated superior performance, achieving an overall accuracy of 86.6% and 80.0% in differentiating between normal, prediabetic, and diabetic individuals based on the optical vascular signals. The proposed non-invasive optical-based approach, coupled with advanced machine learning techniques, holds promise as a potential screening tool for diabetes mellitus. The classification accuracy achieved in this study warrants further investigation and validation in larger and more diverse populations.

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The Relationship Between Computerized Face and Tongue Image Segmentation and Metabolic Parameters in Patients with Type 2 Diabetes Based on Machine Learning

Wen,

Li,

et al. 2024

DMSO

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Thermal Imaging of the Tongue Surface as a Predictive Method in the Diagnosis of Type 2 Diabetes Mellitus

Cited by 3 publications

References 23 publications

The Impact of Disease Duration on Microcirculatory Dysfunction in Young Patients with Uncomplicated Type 1 Diabetes

The Impact of Disease Duration on Microcirculatory Dysfunction in Young Patients with Uncomplicated Type 1 Diabetes

Pioneering diabetes screening tool: machine learning driven optical vascular signal analysis

The Relationship Between Computerized Face and Tongue Image Segmentation and Metabolic Parameters in Patients with Type 2 Diabetes Based on Machine Learning

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