Home Monitoring of Foot Skin Temperatures to Prevent Ulceration

NewsBot · Aug 12, 2023

Prescribing Home Digital Thermometry Coupled with Activity Dosing and Optimized Offloading to Prolong Diabetic Foot Remission: A Case Report
Malindu E Fernando et al
Int J Low Extrem Wounds. 2023 Jul 4

People with a history of diabetic foot ulcers (DFUs) experience diminished health-related quality of life and are at a 40% annual risk of DFU recurrence. Due to a fear of DFU recurrence, people in DFU remission participate less in physical activity and moderate-intensity exercise when compared to people with diabetes who have not had wounds. There is novel evidence to suggest that too little activity during DFU remission contributes to only low magnitudes of repetitive tissue loading creating a higher susceptibility to skin trauma during inadvertent high-activity bouts. Conversely, a hasty return to too much activity could lead to rapid recurrence. There is now high-level evidence from multiple meta-analyses to indicate that home-based foot temperature monitoring, coupled with activity modification and daily inspection of the feet for impending signs of ulceration, could reduce the risk of ulcer recurrence by 50%. There is little evidence, however, to guide the decision-making regarding the appropriate quantity and frequency of physical activity during DFU remission and the acceptability from the patient perspective. This has resulted in limited uptake of this novel intervention in clinical practice. Earlier, we proposed that activity can be dosed for people in foot ulcer remission, just like insulin or medication is dosed. Here, we describe a patient-centered approach to implementing home foot temperature monitoring coupled with daily foot checks and dosage-based return to physical activity in a patient in DFU remission, including his perspective. We believe using such an approach could maximize ulcer-free days in remission, thereby improving quality of life.
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NewsBot · Sep 8, 2023

Automatic Diabetic Foot Ulcer Recognition Using Multi-Level Thermographic Image Data
Ikramullah Khosa et al
Diagnostics (Basel). 2023 Aug 10;13(16):2637

Lower extremity diabetic foot ulcers (DFUs) are a severe consequence of diabetes mellitus (DM). It has been estimated that people with diabetes have a 15% to 25% lifetime risk of acquiring DFUs which leads to the risk of lower limb amputations up to 85% due to poor diagnosis and treatment. Diabetic foot develops planter ulcers where thermography is used to detect the changes in the planter temperature. In this study, publicly available thermographic image data including both control group and diabetic group patients are used. Thermograms at image level as well as patch level are utilized for DFU detection. For DFU recognition, several machine-learning-based classification approaches are employed with hand-crafted features. Moreover, a couple of convolutional neural network models including ResNet50 and DenseNet121 are evaluated for DFU recognition. Finally, a CNN-based custom-developed model is proposed for the recognition task. The results are produced using image-level data, patch-level data, and image-patch combination data. The proposed CNN-based model outperformed the utilized models as well as the state-of-the-art models in terms of the AUC and accuracy. Moreover, the recognition accuracy for both the machine-learning and deep-learning approaches was higher for the image-level thermogram data in comparison to the patch-level or combination of image-patch thermograms.
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NewsBot · Oct 31, 2023

Diabetic Plantar Foot Segmentation in Active Thermography Using a Two-Stage Adaptive Gamma Transform and a Deep Neural Network
Zhenjie Cao et al
Sensors (Basel). 2023 Oct 17;23(20):8511

Pathological conditions in diabetic feet cause surface temperature variations, which can be captured quantitatively using infrared thermography. Thermal images captured during recovery of diabetic feet after active cooling may reveal richer information than those from passive thermography, but diseased foot regions may exhibit very small temperature differences compared with the surrounding area, complicating plantar foot segmentation in such cold-stressed active thermography. In this study, we investigate new plantar foot segmentation methods for thermal images obtained via cold-stressed active thermography without the complementary information from color or depth channels. To better deal with the temporal variations in thermal image contrast when planar feet are recovering from cold immersion, we propose an image pre-processing method using a two-stage adaptive gamma transform to alleviate the impact of such contrast variations. To improve upon existing deep neural networks for segmenting planar feet from cold-stressed infrared thermograms, a new deep neural network, the Plantar Foot Segmentation Network (PFSNet), is proposed to better extract foot contours. It combines the fundamental U-shaped network structure, a multi-scale feature extraction module, and a convolutional block attention module with a feature fusion network. The PFSNet, in combination with the two-stage adaptive gamma transform, outperforms multiple existing deep neural networks in plantar foot segmentation for single-channel infrared images from cold-stressed infrared thermography, achieving an accuracy of 97.3% and 95.4% as measured by Intersection over Union (IOU) and Dice Similarity Coefficient (DSC) respectively.
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NewsBot · Dec 28, 2023

Plantar Thermography in High-Risk Patients With Diabetes Mellitus Compared to Nondiabetic Individuals
Gabriela Verónica Carro et al
Int J Low Extrem Wounds. 2023 Dec 19

Diabetic foot (DF) is one of the most devastating complications of diabetes mellitus (DM). Infrared thermography has been studied for its potential in early diagnosis and preventive measures against DF ulcers, although its role in the management and prevention of DF complications remains uncertain. The objective of this study was to determine the average temperatures of different points of the plantar foot using infrared thermography in patients with DM and history of DF (DFa group, at the highest risk of developing foot ulcers) and compare them to people without DM (NoDM group). One hundred and twenty-three feet were included, 63 of them belonged to DFa Group and the other 60 to NoDM Group. The average temperature in the NoDM Group was 27.4 (26.3-28.5) versus 28.6 (26.8-30.3) in the DFa Group (p = .002). There were differences between both groups in temperatures at the metatarsal heads and heels, but not in the arch. Average foot temperatures did not relate to sex, ankle-brachial index, and age, and had a mild correlation with daily temperature (Spearman 0.51, p < .001). Data provided in our study could be useful in establishing a parameter of normal temperatures for high-risk patients. This could serve as a foundational framework for future research and provide reference values, not only for preventative purposes, as commonly addressed in most studies, but also to assess the applicability of thermography in clinical scenarios particularly when one foot cannot serve as a reference, suspected osteomyelitis of the remaining bone, or instances of increased temperature in specific areas which may necessitate adjustments to the insoles in secondary prevention.
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NewsBot · Jan 4, 2024

Telemedical Monitoring of Plantar Temperature in Diabetic Patients at Risk of Foot Ulcers—The Randomized Smart Prevent Diabetic Feet Trial
Antao Ming, Ahmad Alhajjar, Isabell Walter, Claudia Piehler, Jacqueline Hoetzsch, Martin Leuckert, Vera Clemens, Andreas Petrow, Istiyak M Siddiquee, Florian G Scurt, Berend Isermann, Peter R Mertens
Dtsch Arztebl Int. 2023 Dec 21

Background: The worldwide prevalence of diabetic foot ulcers (DFUs) among persons with diabetes is estimated at 6.3%, with an annual incidence of 9.1 to 26.1 million persons. The early detection of asymmetrical plantar temperature elevation, followed by reduction of weight-bearing on the affected foot, may be an effective mode of prevention.

Methods: Patients with diabetes and peripheral neuropathy (DFU risk groups 2/3) were monitored for plantar abnormalities with a telemedical system consisting of sole inserts with temperature sensors and photographic documentation. An open, prospective, randomized controlled trial was performed to determine whether this system prevented DFUs. The intervention and control groups were also trained in ulcer prevention and observed in follow-up at 6-month intervals for 24 months.

Results: 283 patients were recruited. In 85 137 observation days, DFUs arose in five patients in the control group (n = 143) and in no patient in the intervention group (n = 140). The primary outcome measure was the hazard ratio, which was calculated to be 0.015 (95% confidence interval [0; 19,717]; p = 0.25) after adjustment for age, sex, severity of neuropathy, and risk class. There were 239 alarms and 75 instructions to reduce weight-bearing on the foot. The subjects carried out the telemedical application on about 70% of the days of observation. Quality of life improved in both groups.

Conclusion: The tele-health system used in this trial is practical and enables the early detection of morbidity. Likely explanations for the unexpectedly low ulceration rate in this trial (and, in turn, for the lack of statistical significance) include the availability of a training program and regular follow-up examinations to patients in both arms of the trial, along with lower mobility levels due to the COVID pandemic.
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NewsBot · Jan 31, 2024

Effectiveness of a Continuous Remote Temperature Monitoring Program to Reduce Foot Ulcers and Amputations: Multicenter Postmarket Registry Study
Chia-Ding Shih et al
JMIR Diabetes. 2024 Jan 29:9:e46096

Background: Neuropathic foot ulcers are the leading cause of nontraumatic foot amputations, particularly among patients with diabetes. Traditional methods of monitoring and managing these patients are periodic in-person clinic visits, which are passive and may be insufficient for preventing neuropathic foot ulcers and amputations. Continuous remote temperature monitoring has the potential to capture the critical period before the foot ulcers develop and to improve outcomes by providing real-time data and early interventions. For the first time, the effectiveness of such a strategy to prevent neuropathic foot ulcers and related complications among high-risk patients in a real-world commercial setting is reported.

Objective: This study aims to evaluate the effectiveness of a real-world continuous remote temperature monitoring program in preventing neuropathic foot ulcers and amputations in patients with diabetes.

Methods: In this retrospective analysis of a real-world continuous remote temperature monitoring program, 115 high-risk patients identified by clinical providers from 15 geographically diverse private podiatry offices were analyzed. Patients received continuous remote monitoring socks as part of the program. The enrollment was based on medical necessity as decided by their managing physician. We evaluated data from up to 2 years before enrollment and up to 3 years during the program. The primary outcome was the rate of wound development. Secondary outcomes included amputation rate, the severity of the foot ulcers, and the number of visits to an outpatient podiatry clinic after enrolling in the program.

Results: We observed significantly lower rates of foot ulceration (relative risk reduction [RRR] 0.68; 95% CI 0.52-0.79; number needed to treat [NNT] 5.0; P<.001), less moderate to severe ulcers (RRR 0.86; 95% CI 0.70-0.93; NNT 16.2; P<.001), less amputations (RRR 0.83; 95% CI 0.39-0.95; NNT 41.7; P=.006), and less hospitalizations (RRR 0.63; 95% CI 0.33-0.80; NNT 5.7; P<.002). We found a decrease in outpatient podiatry office visits during the program (RRR 0.31; 95% CI 0.24-0.37; NNT 0.46; P<.001).

Conclusions: Our findings suggested that a real-world continuous remote temperature monitoring program was an effective strategy to prevent foot ulcer development and nontraumatic foot amputation among high-risk patients.
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NewsBot · Feb 20, 2024

Automated Prediction of Diabetes Mellitus using Infrared Thermal Foot Images: Recurrent Neural Network Approach
Gulshan Kumar, Ajat Shatru Arora
Biomed Phys Eng Express. 2024 Jan 31.

Millions of people worldwide are affected by Diabetes Mellitus (DM), which is a chronic disease. Evaluation of the DM indicator, namely blood glucose level, requires invasive methods such as glucometer or blood tests, which cause discomfort to the patient. Automated noninvasive monitoring methods are urgently needed to ensure consistency and better treatment. The regular monitoring of DM can prevent or delay the onset of complications. Thermal foot images have been proposed as noninvasive methods for the prediction of DM. Thermograms were acquired at Mittal Eye Hospital, Sangrur, India, from 50 participants in the diabetic (without neuropathic conditions) and non-diabetic groups using a thermal camera (FLIR E-60). This study proposes an automated prediction system for DM using thermal foot images and Recurrent Neural Network (RNN) approach. The proposed system processes the thermal images and extracts relevant features using a CNN (Convolutional Neural Network). The extracted features were then fed to the RNN to predict the presence or absence of the DM. The experimental results demonstrate that the proposed framework attains an accuracy of (97.14 ± 1.5) %, surpassing the predictive capabilities of light-weight convolutional neural network (Lw-CNN), which only achieves an accuracy of (82.9 ± 3) % in predicting DM. This performance outperformed other state-of-the-art methods in the field. Our approach has the potential to be used as prediction tool for DM. Therefore, the proposed system has the potential for prediction of DM and improve patient outcomes by enabling timely intervention. Future work should focus on evaluating the proposed system on a larger dataset and integrating it with clinical decision support systems for personalized care. This study holds the promise of transforming DM screening and diagnosis, leading to enhanced patient outcomes.
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NewsBot · Mar 2, 2024

Clinical value of foot thermometry in patients with acute limb ischemia
Dumitru Casian et al
Vascular. 2024 Mar 1

Objective: We hypothesized that the severity of foot poikilothermy can be used for better differentiation of grades of acute limb ischemia. Thus, the study aim was evaluation of the value of non-contact foot thermometry, performed using a low-cost infrared medical thermometer, as an adjunct for clinical diagnosis of immediately threatened acute limb ischemia.

Methods: It was a single-center observational prospective study performed over 3 years. Patients with acute limb ischemia of lower limbs grade I-IIB Rutherford treated with urgent revascularization were included. Grade of ischemia was determined independently by two experienced vascular surgeons. Thermometry of the ischemic foot was performed using a medical digital infrared non-contact thermometer (CK-T1501, Cooligg, China) with measuring accuracy of ±0.2°C. Temperature was measured in three points: the dorsal surface of the foot, plantar surface of the foot (both in the metatarsal region), and forehead. The maximal temperature gradient between patient's forehead and foot (∆Tmax F-F) was calculated. Measurements were repeated 6-12 h after revascularization.

Results: A total of 147 patients were included. Only 3 (2%) patients presented rest pain without sensory loss and motor deficit, while the majority were diagnosed with mild (63/147, 42.8%) or moderate (27/147, 18.3%) motor deficit. The temperature of the ischemic foot varied from 20 to 36.1°C, while median value of the temperature was 26.7 [24.5-29.9] °C on the dorsal surface and 26.8 [24.5-29.6] °C on the plantar surface of the foot (p = 0.85). Patients with Grade IIB ischemia had significantly lower dorsal foot temperature, plantar foot temperature, and larger ∆Tmax F-F than the patients with grades I-IIA: 25.1 [23.9-26.8] °C versus 29.9 [27.6-30.8] °C; 25.2 [23.8-27.5] °C versus 29.6 [28-31.1] °C; and 11.6 [9.7-12.8] °C versus 7.2 [6-9] °C (p < 0.0001). Areas under ROC curve for diagnosis of Grade IIB ischemia were similar for dorsal foot temperature (0.82), plantar temperature (0.81), and ∆Tmax F-F (0.82). The best cutoff value by Youden was ≥9.5°C for ∆Tmax F-F, ≤26.8°C for dorsal, and ≤27.7°C for plantar temperature. Criterion ∆Tmax F-F offered the highest specificity of 86% (95%CI 74.2-93.7) and positive predictive value of 89.2% (95%CI 79.5-93.2), while plantar temperature offered sensitivity of 82.5% (95%CI 70.1-91.3) and negative predictive value of 69.1% (95%CI 57.6-83.2). In multivariate analysis including age, gender, and etiology of arterial occlusion, the criterion ∆Tmax F-F of ≥9.5°C was a unique variable significantly associated with risk of amputation (adjusted OR 2.6, 95%CI 1.2-5.9, p = 0.01).

Conclusion: Current study demonstrated that patients with immediately threatening ALI have significantly lower foot temperature than those with viable and marginally threatened limbs. Severe foot poikilothermy at admission is associated with poor outcomes of revascularization, mostly with limb loss.
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NewsBot · Apr 29, 2024

Assessing equity in the uptake of remote foot temperature monitoring in a large integrated US healthcare system
Alyson J Littman et al
PLoS One. 2024 Apr 1;19(4)

Objective: We assessed equity in the uptake of remote foot temperature monitoring (RTM) for amputation prevention throughout a large, integrated US healthcare system between 2019 and 2021, including comparisons across facilities and between patients enrolled and eligible patients not enrolled in RTM focusing on the Reach and Adoption dimensions of the Reach, Effectiveness, Adoption, Implementation, and Maintenance (RE-AIM) framework.

Material and methods: To assess whether there was equitable use of RTM across facilities, we examined distributions of patient demographic, geographic, and facility characteristics across facility RTM use categories (e.g., no RTM use, and low, moderate, and high RTM use) among all eligible patients (n = 46,294). Second, to understand whether, among facilities using RTM, there was equitable enrollment of patients in RTM, we compared characteristics of patients enrolled in RTM (n = 1066) relative to a group of eligible patients not enrolled in RTM (n = 27,166) using logistic regression and including all covariates.

Results: RTM use increased substantially from an average of 11 patients per month to over 40 patients per month between 2019 and 2021. High-use RTM facilities had higher complexity and a lower ratio of patients per podiatrist but did not have consistent evidence of better footcare process measures. Among facilities offering RTM, enrollment varied by age, was inversely associated with Black race (vs. white), low income, living far from specialty care, and being in the highest quartiles of telehealth use prior to enrollment. Enrollment was positively associated with having osteomyelitis, Charcot foot, a partial foot amputation, BMI≥30 kg/m2, and high outpatient utilization.

Conclusions: RTM growth was concentrated in a small number of higher-resourced facilities, with evidence of lower enrollment among those who were Black and lived farther from specialty care. Future studies are needed to identify and address barriers to uptake of new interventions like RTM to prevent exacerbating existing ulceration and amputation disparities.
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NewsBot · May 17, 2024

Cost-effectiveness of temperature monitoring to help prevent foot ulcer recurrence in people with diabetes: A multicenter randomized controlled trial
Jaap J Van Netten et al
Diabetes Metab Res Rev. 2024 May;40(4):e3805

Aims: Diabetes-related foot ulcers are common, costly, and frequently recur. Multiple interventions help prevent these ulcers. However, none of these have been prospectively investigated for cost-effectiveness. Our aim was to evaluate the cost-effectiveness of at-home skin temperature monitoring to help prevent diabetes-related foot ulcer recurrence.

Materials and methods: Multicenter randomized controlled trial. We randomized 304 persons at high diabetes-related foot ulcer risk to either usual foot care plus daily at-home foot skin temperature monitoring (intervention) or usual care alone (control). Primary outcome was cost-effectiveness based on foot care costs and quality-adjusted life years (QALY) during 18 months follow-up. Foot care costs included costs for ulcer prevention (e.g., footwear, podiatry) and for ulcer treatment when required (e.g., consultation, hospitalisation, amputation). Incremental cost-effectiveness ratios were calculated for intervention versus usual care using probabilistic sensitivity analysis for willingness-to-pay/accept levels up to €100,000.

Results: The intervention had a 45% probability of being cost-effective at a willingness-to-accept of €50,000 per QALY lost. This resulted from (non-significantly) lower foot care costs in the intervention group (€6067 vs. €7376; p = 0.45) because of (significantly) fewer participants with ulcer recurrence(s) in 18 months (36% vs. 47%; p = 0.045); however, QALYs were (non-significantly) lower in the intervention group (1.09 vs. 1.12; p = 0.35), especially in those without foot ulcer recurrence (1.09 vs. 1.17; p = 0.10).

Conclusions: At-home skin temperature monitoring for diabetes-related foot ulcer prevention compared with usual care is at best equally cost-effective. The intervention resulted in cost-savings due to preventing foot ulcer recurrence and related costs, but this came at the expense of QALY loss, potentially from self-monitoring burdens.
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NewsBot · May 23, 2024

Effectiveness of a Continuous Remote Temperature Monitoring Program to Reduce Foot Ulcers and Amputations: Multicenter Postmarket Registry Study
Chia-Ding Shih et al
Source

Background:
Neuropathic foot ulcers are the leading cause of nontraumatic foot amputations, particularly among patients with diabetes. Traditional methods of monitoring and managing these patients are periodic in-person clinic visits, which are passive and may be insufficient for preventing neuropathic foot ulcers and amputations. Continuous remote temperature monitoring has the potential to capture the critical period before the foot ulcers develop and to improve outcomes by providing real-time data and early interventions. For the first time, the effectiveness of such a strategy to prevent neuropathic foot ulcers and related complications among high-risk patients in a real-world commercial setting is reported.

Objective:
This study aims to evaluate the effectiveness of a real-world continuous remote temperature monitoring program in preventing neuropathic foot ulcers and amputations in patients with diabetes.

Methods:
In this retrospective analysis of a real-world continuous remote temperature monitoring program, 115 high-risk patients identified by clinical providers from 15 geographically diverse private podiatry offices were analyzed. Patients received continuous remote monitoring socks as part of the program. The enrollment was based on medical necessity as decided by their managing physician. We evaluated data from up to 2 years before enrollment and up to 3 years during the program. The primary outcome was the rate of wound development. Secondary outcomes included amputation rate, the severity of the foot ulcers, and the number of visits to an outpatient podiatry clinic after enrolling in the program.

Results:
We observed significantly lower rates of foot ulceration (relative risk reduction [RRR] 0.68; 95% CI 0.52-0.79; number needed to treat [NNT] 5.0; P<.001), less moderate to severe ulcers (RRR 0.86; 95% CI 0.70-0.93; NNT 16.2; P<.001), less amputations (RRR 0.83; 95% CI 0.39-0.95; NNT 41.7; P=.006), and less hospitalizations (RRR 0.63; 95% CI 0.33-0.80; NNT 5.7; P<.002). We found a decrease in outpatient podiatry office visits during the program (RRR 0.31; 95% CI 0.24-0.37; NNT 0.46; P<.001).

Conclusions:
Our findings suggested that a real-world continuous remote temperature monitoring program was an effective strategy to prevent foot ulcer development and nontraumatic foot amputation among high-risk patients
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NewsBot · Jun 5, 2024

Thermography of the Plantar Region Alone is Not Sufficient to Detect All Areas at Risk of Ulceration in Diabetic Foot Patients

NewsBot · Jul 25, 2024

Can thermography predict diabetic foot ulcer risk in patients with diabetes mellitus? A systematic review
Divyeshz Thakku Sivakumar et al
J Tissue Viability. 2024 Jun 29

Background: There is a growing prevalence of diabetic foot ulcers (DFUs) in patients with diabetes mellitus and the use of thermography has sparked interest in a non-invasive diagnostic method for early DFU risk assessment and management.

Aim: This systematic review aims to assess the use of thermography in predicting diabetic foot ulcer risk in patients with diabetes mellitus.

Methods: A systematic search of publications using MEDLINE, CINAHL, and Cochrane databases was conducted in April 2023, and relevant articles were reviewed. Data was extracted and a narrative synthesis was undertaken. The evidence-based librarianship (EBL) checklist assessed the methodological quality of the studies included. Reviewing these articles to the primary and secondary outcomes of this literature review. The primary outcome focused on the predictive capabilities of thermography for DFU prediction, while the secondary outcome assessed the feasibility, usability, and effectiveness of thermography.

Results: Eight studies were conducted from 1994 to 2021 with an emphasis on the predictability of thermography in predicting DFU risk. All eight studies focused on temperature variations associated with DFU development. Six of the included studies compared the effectiveness of DFU occurrence in diabetic patients and non-DFU use. The overall results showed that employing thermography in DFU prevention might allow for early detection and intervention, offering a non-invasive and effective means to reduce the risk of DFU development and its associated complications in patients with diabetes mellitus.

Conclusion: The systematic review indicates that thermography holds promise for predicting DFU risk, with studies showcasing predictive capabilities and patient benefits. Despite some challenges and limitations, the evidence suggests thermography's value in assessing DFU risk in diabetes patients, warranting further research on device types and locations.
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NewsBot · Jul 28, 2024

Thermography Research in Diabetic Foot: Insights From a Scopus-Based Bibliometric Study
Shukriah Aqilah Zakaria et al
Cureus. 2024 Jun 21;16(6):e62858

Diabetic foot problems are among the most debilitating complications of diabetes mellitus. These problems incur significant economic costs and reduce quality of life. The integration of thermography technology in the screening and management of diabetic foot problems has been proven to be successful in recent years. By detecting changes in temperature, thermography helps identify early infections and assists in patient monitoring. These early successes have inspired more research and publications in this field. To date, a comprehensive bibliometric analysis of thermography-related research on diabetic foot using the Scopus database has not been conducted. This bibliometric analysis aims to fill this gap by reviewing the Scopus database from its inception until 2023 to examine the literature on thermography-related research on diabetic foot. A total of 342 articles met the selection criteria and were included in this analysis. The number of articles in this field remained low until the 2010s, when there was a sudden surge of interest that prompted numerous publications. Authors from the USA contributed the highest number of articles and had the greatest scholarly impact in this field. Despite the major contribution from the USA, there were numerous collaborations between various countries, underscoring the importance of international collaboration in advancing research and exchanging knowledge
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NewsBot · Oct 27, 2024

A Self-Powered Wireless Temperature Sensor Platform for Foot Ulceration Monitoring
Joseph Agyemang Duah et al
Sensors (Basel). 2024 Oct 12;24(20):6567

This work describes a self-powered wireless temperature sensor platform that can be used for foot ulceration monitoring for diabetic patients. The proposed self-powered sensor platform consists of a piezoelectric bimorph, a power conditioning circuit, a temperature sensor readout circuit, and a wireless module. The piezoelectric bimorph mounted inside the shoe effectively converts the foot movement into electric energy that can power the entire sensor platform. Furthermore, a sensor platform was designed, considering the energy requirement of 4.826 mJ for transmitting one data packet of 18 bytes. The self-powered sensor platform prototype was evaluated with five test subjects with different weights and foot shapes; the test results show the subjects had to walk an average of 119.6 s to transmit the first data packet and an additional average of 71.2 s to transmit the subsequent data packet. The temperature sensor showed a resolution of 0.1 °C and a sensitivity of 56.7 mV/°C with a power conditioning circuit efficiency of 74.5%.
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NewsBot · Nov 4, 2024

Cost-effectiveness of temperature monitoring to help prevent foot ulcer recurrence in people with diabetes: A multicenter randomized controlled trial
Jaap J Van Netten et al
Diabetes Metab Res Rev. 2024 May;40(4):e3805

Aims: Diabetes-related foot ulcers are common, costly, and frequently recur. Multiple interventions help prevent these ulcers. However, none of these have been prospectively investigated for cost-effectiveness. Our aim was to evaluate the cost-effectiveness of at-home skin temperature monitoring to help prevent diabetes-related foot ulcer recurrence.

Materials and methods: Multicenter randomized controlled trial. We randomized 304 persons at high diabetes-related foot ulcer risk to either usual foot care plus daily at-home foot skin temperature monitoring (intervention) or usual care alone (control). Primary outcome was cost-effectiveness based on foot care costs and quality-adjusted life years (QALY) during 18 months follow-up. Foot care costs included costs for ulcer prevention (e.g., footwear, podiatry) and for ulcer treatment when required (e.g., consultation, hospitalisation, amputation). Incremental cost-effectiveness ratios were calculated for intervention versus usual care using probabilistic sensitivity analysis for willingness-to-pay/accept levels up to €100,000.

Results: The intervention had a 45% probability of being cost-effective at a willingness-to-accept of €50,000 per QALY lost. This resulted from (non-significantly) lower foot care costs in the intervention group (€6067 vs. €7376; p = 0.45) because of (significantly) fewer participants with ulcer recurrence(s) in 18 months (36% vs. 47%; p = 0.045); however, QALYs were (non-significantly) lower in the intervention group (1.09 vs. 1.12; p = 0.35), especially in those without foot ulcer recurrence (1.09 vs. 1.17; p = 0.10).

Conclusions: At-home skin temperature monitoring for diabetes-related foot ulcer prevention compared with usual care is at best equally cost-effective. The intervention resulted in cost-savings due to preventing foot ulcer recurrence and related costs, but this came at the expense of QALY loss, potentially from self-monitoring burdens.
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NewsBot · Nov 6, 2024

Remote Foot Temperature Monitoring Among Veterans: Large Observational Study of Noncompliance and Its Correlates
Alyson J Littman et al
JMIR Diabetes. 2024 Nov 5

Background: In-home remote foot temperature monitoring (RTM) holds promise as a method to reduce foot ulceration in high-risk patients with diabetes. Few studies have evaluated adherence to this method or evaluated the factors associated with noncompliance.

Objective: The aims of this study were to estimate noncompliance in patients who were enrolled in RTM nationwide across Department of Veterans Affairs (VA) and to evaluate characteristics associated with noncompliance.

Methods: We conducted an observational study including 1137 patients in the VA who were enrolled in RTM between January 2019 and June 2021, with follow-up through October 2021. Patient information was obtained from the VA's electronic health record and RTM use was obtained from the company. Noncompliance was defined as using the mat <2 days per week for ≥4 of the 12 months of follow-up. Using a multivariable model, we calculated odds ratios (ORs) and 95% CIs for associations between various factors and noncompliance and compared using Akaike information criterion statistics, a measure of model fit.

Results: The sample was predominantly male (n=1125, 98.94%) ; 21.1% (n=230) were Black and 75.7% (n=825) were White. Overall, 37.6% (428/1137) of patients were classified as noncompliant. In the multivariable model, an intermediate area deprivation index was statistically significantly and inversely associated with noncompliance (area deprivation index 50-74 vs 1-24; OR 0.56, 95% CI 0.35-0.89); factors significantly and positively associated with noncompliance included recent history of osteomyelitis (OR 1.44, 95% CI 1.06-1.97), Gagne comorbidity index score ≥4 (vs ≤0; OR 1.81, 95% CI 1.15-2.83), telehealth encounters (28+ vs <6; OR 1.70, 95% CI 1.02-2.84), hemoglobin A1c≥10 (vs <5.7; OR 2.67, 95% CI 1.27-5.58), and current smoking (OR 2.06, 95% CI 1.32-3.20). Based on Akaike information criterion differences, the strongest factors associated with noncompliance were behavioral factors (poor glucose control [as measured by hemoglobin A1c] and smoking), and to a lesser extent, factors such as a recent history of osteomyelitis and an elevated Gagne comorbidity index, indicating a high comorbidity burden.

Conclusions: To reduce the risk of ulcer recurrence and amputation, proactively providing additional support for self-monitoring to patients with characteristics identified in this study (poor glucose control, current smoking, high comorbidity burden) may be helpful. Furthermore, research is needed to better understand barriers to use, and whether the addition of design features, reminders, or incentives may reduce noncompliance and the risk of foot ulcers.
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NewsBot · Feb 19, 2025

The utility of skin surface temperature measurement in the prediction of diabetic foot ulceration
Pauline Wilson et al
J Tissue Viability. 2025 Jan 11;34(2):100851.

Aim: Temperature is a marker of inflammation. Research has shown that by measuring skin surface temperature in those with a history of diabetic foot ulceration (DFU), tissue damage can be identified early and reduce rates of subsequent DFU. This study considered whether measuring skin surface temperature using infra-red thermometry can identify early tissue damage and inform clinical practice in a general population with Diabetes Mellitus (DM).

Method: The data presented was taken from a prospective observational study of risk factors in patients with Diabetes. As part of this study, temperature measurements using the Flir E6 Infra-red camera were taken from 216 individuals attending outpatient diabetes clinics in a large urban teaching hospital in Ireland as part of a comprehensive diabetic foot assessment. Measurements were taken at foot sites associated with increased risk at baseline received repeat temperature measurement on 2 subsequent occasions in the following week. Any ulceration was subsequently recorded.

Results: Discussion: Of the 216 participants, 4 % (n = 9) developed visual ulceration within 7 days. All of these had been identified at high risk at baseline assessment and had a history of foot disease. A temperature differential of ≥2.2 °C between sites of interest was recorded. Temperature ranges varied from 29.1 °C to 37.6 °C. High temperature was considered if over 35 °C. Of those who developed a diabetic foot ulceration within the 7 day follow up period, 78 % (n = 7) had a high temperature. The remaining 22 % (n = 2) who developed ulceration had a temperature reading in the normal range.

Conclusion: Elevated skin surface temperature was seen prior to the development of DFU in all cases in this study. Initial analysis suggests that neither the 35 °C threshold nor a 2.2 °C temperature difference is indicative of inevitable tissue damage in this population due to the high number of false positives seen.
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NewsBot · Aug 17, 2025

Advancing Diabetic Foot Ulcer
Detection: A Study of Thermographic
Imaging with Faster Vision Transformer
A M Arunnagiri et al
Source

Background: Diabetic foot ulceration (DFU), a serious diabetes mellitus com-
plication, is associated with high risks of infection, limb amputation, and life-
threatening events. Objective: The present study explores the efficacy of deep
learning in enhancing DFU diagnosis using infrared thermography imaging,
with a focus on overcoming challenges in comprehensively evaluating plan-
tar temperature differences—a determining factor among diabetic patients.
Methods: By comparing deep learning architecture with traditional machine
learning methods, the study aims to identify the most effective approaches for
early detection and intervention to prevent complications. The Faster Vision
Transformer (FasterViT) is employed as the basic architecture due to its estab-
lished efficiency in processing medical imaging data and its ability to rapidly
extract clinically relevant features, in line with the timeliness required in real-
world clinical practice. Findings: FasterViT achieved a breathtaking classifica-
tion accuracy of 98%, a pointer to its potential in revolutionizing DFU diagno-
sis. Novelty and applications: The findings underscore the revolutionary role
of AI-based systems in improving diagnostic accuracy, enabling timely clinical
decision-making, and, by extension, advancing patient care outcomes in dia-
betes management
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NewsBot · Aug 20, 2025

Foot Temperature Monitoring in People With Diabetes Mellitus: A Systematic Review of Incidence and Risk With Meta-Analysis
Sónia A. L. Novais, Maribel D. Carvalhais, Diana F. C. Ribeiro, Íris N. D. Correia, Victoria Sandutã, Ester Goutan-Roura, Paulo A. O. Marques
15 August 2025

Diabetic foot ulcers represent a serious complication of diabetes mellitus, often associated with amputations and hospitalizations. Monitoring foot temperature allows early detection of the risk of developing DFUs. This study compares foot temperature monitoring methods in patients with diabetes mellitus and assesses their applicability in the early detection of foot temperature differences. A systematic literature review was conducted following Cochrane methodology. An electronic search was carried out across multiple scientific databases. The risk of bias was assessed using the Rob2 tool, and the meta-analysis was performed using the Cochrane platform, Review Manager 5.4. Eight studies were included, seven of which met the criteria for meta-analysis. The results indicate that diabetic foot ulcers and amputations were less frequent in the temperature monitoring group compared to the control groups. The meta-analysis underscores the potential of foot temperature monitoring to prevent diabetic foot ulcers and their recurrence. Nurses should implement interventions that promote temperature monitoring and adherence to preventive measures while raising awareness of the benefits of these actions.
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NewsBot · Sep 2, 2025

Analysis of foot thermography images of diabetic patients using artificial intelligence: a scoping review
Riki Wartakusumah et al
Diabetes Res Clin Pract. 2025 Aug 30:112446

Artificial intelligence (AI) enhances thermal image analysis by providing advanced pattern recognition, improving the accuracy of diabetic foot condition detection. AI-driven thermography systems support clinicians, but research on AI for diabetic foot thermography is fragmented, with diverse algorithms and existing reviews focusing mainly on statistical performance. This review aimed to provide a comprehensive review of AI-based diabetic foot thermography, with a focus on condition detection, performance metrics, clinical implications, and existing research gaps. A scoping review was conducted using PubMed, MEDLINE, CINAHL, Science Direct, Scopus, and Google Scholar with keywords "diabetic foot temperature," "thermal imaging," and "artificial intelligence," including related MeSH terms. Eligible studies included original research and conference proceedings on AI-based foot thermography for diagnosing or monitoring diabetic adults. Literature reviews and meta-analyses were excluded. Sixty articles were reviewed. Most addressed increased temperature pattern, followed by decreased temperature pattern, and DFU severity classification pattern. AI performance ranged from 61% to 100%. Study environments were 46.7% controlled, 6.6% uncontrolled, and 46.7% unreported. AI applications included clinical decision support, remote monitoring, and reducing clinician workload. AI has advanced diabetic foot detection, However, additional studies in uncontrolled environments are needed to improve accuracy and enhance generalizability under real-world conditions.
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NewsBot · Sep 15, 2025

Exploring the Value of Continuous Plantar Temperature Monitoring for Diabetic Foot Health Management: Observational, Prospective Cohort Study
Maryam Hajizadeh et al
JMIR Mhealth Uhealth. 2025 Sep 12:13:e73187.

Background: Diabetic foot ulcers (DFUs) are a life-changing complication of diabetes. There is increasing evidence that remote plantar temperature monitoring can reduce the recurrence of DFUs. Monitoring of foot temperature once a day is the current guideline for identifying early signs of foot inflammation. However, single readings of physiological signals can increase the risk of misdiagnosis when the signals fluctuate throughout the day.

Objective: The aim of this study was to evaluate whether intraday temperature asymmetry signals were stable or varied as a function of time in individuals at risk of DFUs.

Methods: In total, 64 participants with diabetes (mean age 68, SD 13.8 y) were provided with multimodal sensory insoles (Orpyx Sensory Insoles) to monitor continuous temperature data at a frequency of once per minute at 5 plantar locations in a 90-day study window. The augmented Dickey-Fuller test was used to determine whether the temperature asymmetry signals were stationary (ie, indicating constant mean and variance over time) or nonstationary (ie, indicating time-varying behaviors or trends in the signal).

Results: The study included 43 participants, 1080 data days, and 5400 contralateral temperature asymmetry signals. Most (4428/5400, 82%) of the temperature asymmetry signals were nonstationary, with intraday fluctuations likely influenced by physiological and environmental factors. Of the nonstationary signals, nearly half (1948/4428, 44%) fluctuated above and below the concerning asymmetry threshold of 2.2 °C. The intraday variability underscores the potential for false-positive and false-negative hot spot detection with once-daily measurements. Substantial variability was observed in stationarity patterns both within and across participants. Notably, concerning asymmetries in nonstationary signals occurred at different time points across participants, measurement windows, and days.

Conclusions: Our findings highlight the value of continuous plantar temperature monitoring for diabetic foot health management, relative to once-daily measurements. Several repeated measurements throughout the day increase confidence with regard to the accuracy of observed plantar physiology trends. Continuous monitoring may improve the accuracy of plantar temperature measurement, unlock new diagnostic capabilities, and support personalized care.
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NewsBot · Sep 24, 2025

Exploring the Value of Continuous Plantar Temperature Monitoring for Diabetic Foot Health Management: Observational, Prospective Cohort Study
Maryam Hajizadeh1 Author Orcid Image ; Emily Matijevich1 Author Orcid Image ; Emily Bray1 Author Orcid Image ; Evan Minty2 Author Orcid Image ; Brock Liden3 Author Orcid Image
Source

Background: Diabetic foot ulcers (DFUs) are a life-changing complication of diabetes. There is increasing evidence that remote plantar temperature monitoring can reduce the recurrence of DFUs. Monitoring of foot temperature once a day is the current guideline for identifying early signs of foot inflammation. However, single readings of physiological signals can increase the risk of misdiagnosis when the signals fluctuate throughout the day.

Objective: The aim of this study was to evaluate whether intraday temperature asymmetry signals were stable or varied as a function of time in individuals at risk of DFUs.

Methods: In total, 64 participants with diabetes (mean age 68, SD 13.8 y) were provided with multimodal sensory insoles (Orpyx Sensory Insoles) to monitor continuous temperature data at a frequency of once per minute at 5 plantar locations in a 90-day study window. The augmented Dickey-Fuller test was used to determine whether the temperature asymmetry signals were stationary (ie, indicating constant mean and variance over time) or nonstationary (ie, indicating time-varying behaviors or trends in the signal).

Results: The study included 43 participants, 1080 data days, and 5400 contralateral temperature asymmetry signals. Most (4428/5400, 82%) of the temperature asymmetry signals were nonstationary, with intraday fluctuations likely influenced by physiological and environmental factors. Of the nonstationary signals, nearly half (1948/4428, 44%) fluctuated above and below the concerning asymmetry threshold of 2.2 °C. The intraday variability underscores the potential for false-positive and false-negative hot spot detection with once-daily measurements. Substantial variability was observed in stationarity patterns both within and across participants. Notably, concerning asymmetries in nonstationary signals occurred at different time points across participants, measurement windows, and days.

Conclusions: Our findings highlight the value of continuous plantar temperature monitoring for diabetic foot health management, relative to once-daily measurements. Several repeated measurements throughout the day increase confidence with regard to the accuracy of observed plantar physiology trends. Continuous monitoring may improve the accuracy of plantar temperature measurement, unlock new diagnostic capabilities, and support personalized care.
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NewsBot · Nov 30, 2025

AI-Powered Thermography for Diabetic Foot Risk Stratification: Multicenter Cross-Sectional Study
Meshari F Alwashmi et al
Source

Background: Diabetic foot complications are among the most severe and costly outcomes associated with diabetes, with high prevalence particularly in the Middle East and North Africa region. Current screening tools are often limited by subjectivity, invasiveness, or scalability challenges, underscoring the need for innovative approaches.

Objective: This multicenter study aimed to evaluate the performance of an artificial intelligence (AI)–powered thermographic system, Thermal Foot Scan (TFScan), in identifying patients at elevated risk of diabetic foot complications through noninvasive temperature profiling.

Methods: A multicenter cross-sectional analysis of deidentified routine screening data across 4 regions in Saudi Arabia was conducted enrolling 1120 individuals with diabetes. Participants underwent thermal imaging using a smartphone-compatible infrared camera with AI algorithms analyzing angiosomal temperature patterns and asymmetries. Risk was stratified into 4 categories (very low, low, moderate, and high). Associations between TFScan classifications and clinical risk factors, symptoms of neuropathy, and thermal abnormalities were assessed.

Results: While 90.7% (1016/1120) of the participants were classified as very low or low risk, 9.3% (104/1120) were identified as moderate or high risk. This higher-risk group exhibited significantly greater prevalence of key diabetic complications (P<.001). Peripheral artery disease was present in 20.2% (21/104) of moderate- and high-risk participants compared to just 0.8% (8/1016) of lower-risk individuals. Cardiovascular disease (60/104, 57.7% vs 313/1016, 30.8%), neuropathy (12/104, 11.5% vs 37/1016, 3.6%), foot deformities (15/104, 14.4% vs 6/1016, 0.6%), and symptoms of loss of protective sensation (53/104, 51% vs 354/1016, 34.8%) were all significantly more frequent in the high-risk subgroup than in the low-risk group, respectively. Thermal imaging further revealed pronounced abnormalities: temperature asymmetries of ≥2.2 °C were observed in 7.1% (79/1120) of the patients overall, with the highest asymmetry and thermal change index scores concentrated in the moderate- and high-risk groups. These individuals also exhibited greater deviations in angiosomal temperature differences—exceeding 2.2 °C in key vascular territories such as the medial plantar and lateral plantar arteries—suggesting both early inflammatory states and critical perfusion deficits.

Conclusions: The TFScan system effectively stratified patients with diabetes into clinically meaningful risk categories, with moderate- and high-risk groups exhibiting a significantly higher burden of vascular, neuropathic, and thermal abnormalities. However, the cross-sectional design, partial reliance on self-report, and low prevalence of advanced complications may limit causal inference. These findings highlight the potential of AI-enhanced thermography to serve as a scalable, objective screening tool for proactive diabetic foot management. Further longitudinal studies are warranted to validate its predictive power and support widespread clinical adoption.
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NewsBot · Dec 4, 2025

An Investigation of Foot Temperature Deviations in Individuals with Diabetes: Insights from Wearable In-Shoe Technology
Mark Borg, Stephen Mizzi, Robert Farrugia, Tiziana Mifsud, Anabelle Mizzi, Josef Bajada, Owen Falzon
Annu Int Conf IEEE Eng Med Biol Soc. 2025 Jul:2025:1-7.

Plantar foot temperature is a valuable indicator of diabetes-related complications, but traditional assessment methods, such as infrared thermography and contact thermometers, require unshod feet and controlled conditions, limiting their practicality for continuous monitoring. In this study, we employ a smart insole with 21 embedded temperature sensors to capture plantar temperature data from shod feet. We introduce a novel approach that leverages per-foot relative temperature values-normalized to the foot's mean-rather than absolute values or inter-foot asymmetry. Using data collected during static postures (lying, sitting, and standing), we evaluate multiple machine learning classifiers, with Random Forest achieving the highest accuracy (83.20%), alongside high sensitivity (93.75%) but moderate specificity (63.6%). To enhance explainability, we apply SHAP analysis to interpret model predictions and identify key sensor contributions. Additionally, we derive simple decision rules from the Random Forest model, finding that two medial arch sensors can achieve near-equivalent accuracy (80.38% and 79.82%) to the full model. These results suggest that deviations in plantar temperature patterns could serve as an indicator of diabetes-related foot health changes. Future work will expand this approach to ambulatory activities, integrating static and dynamic features to develop an insole-based system for continuous foot health monitoring in real-world settings.Clinical relevance- This study demonstrates that plantar temperature patterns, captured via smart insoles, can reflect changes associated with diabetes-related foot health conditions. The identification of simple, high-accuracy decision rules using medial arch sensors suggests a potential low-cost, non-invasive tool for detecting thermal irregularities linked to diabetes-related foot complications.
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NewsBot · Dec 5, 2025

Bio-Inspired DFU-ThermoNet: A Spiking
Transformer-Driven Framework for Early
Diagnosis and Risk Prediction of Diabetic
Foot Ulcers Using Thermal Imaging
R. HARIS AND RAJA MARAPPAN
Source

ABSTRACT Diabetic Foot Ulcers (DFUs) are among the most serious problems for people with diabetes,
often leading to infection and amputation. Early identification of risk levels can prevent severe complications.
DFU risk evaluation using foot thermal imaging has gained attention with recent deep learning (DL)
advances. Current practices focus on subjective clinical evaluation, which relies on subjective clinical
assessment and manual visual inspection, often causing delays in diagnosis. We propose DFU-ThermoNet,
a bio-inspired DL framework for early DFU diagnosis and risk prediction using plantar thermal images. The
model combines a Spiking Transformer Block (STB) for temporal pattern recognition, a Multi-Scale Capsule
Network (MCN) for preserving spatial hierarchies, and a Dynamic Attention Fusion (DAF) mechanism to
effectively merge features from different modules. A self-attention convolutional layer further enhances the
discrimination of local features. Before training, the thermal images underwent a structured preprocessing
pipeline including Thermal Change Index (TCI) calculation, risk-level classification into five categories—
normal, mild, moderate, high, and critical risk—and class-balanced augmentation with perceptual hashing to
prevent duplication. Training and testing were conducted using a subject-wise 80-20% split. This integrated
strategy ensures that DFU-ThermoNet captures fine-grained spatial details and temporal changes critical
for accurate DFU risk classification. DFU-ThermoNet was trained and tested on the Plantar Thermogram
Database (PTD), which consists of 6000 thermal foot images, and the following measures were attained.
Classification accuracy: 98.67%; Precision: 0.99; Recall: 0.99; F1 score: 0.99; and ROC-AUC score:
0.99 across the five risk categories, subject-wise 5-fold cross-validation confirmed consistent performance
(97.84 %±0.53 %) with 95% confidence interval (CI), demonstrating the dependability of the proposed
framework. DFU-ThermoNet provides an accurate, non-invasive assessment for early DFU risk detection
compared with the state-of-the-art models.
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NewsBot · Dec 28, 2025

Plantar Thermogram Analysis Using Deep Learning for Diabetic Foot Risk Classification
Vipawee Panamonta et al
J Diabetes Sci Technol. 2025 Feb 20:19322968251316563.

Aims: Thermography is a noninvasive method to identify patients at risk of diabetic foot ulcers. In this study, we employed thermography and deep learning to stratify patients with diabetes at risk of developing foot ulcers.

Methods: We prospectively recorded clinical data and plantar thermograms for adult patients with diabetes who underwent diabetic foot screening. A total of 153 thermal images were analyzed using a deep learning algorithm to determine the risk of diabetic foot ulcers. The neural network was trained using a balanced dataset consisting of 98 thermal images (49 normal and 49 abnormal), with 80% allocated for training and 20% for validation. The trained model was then validated on a separate testing dataset consisting of 55 thermal images (42 normal and 13 abnormal). The neural network was trained to prioritize higher sensitivity in identifying at-risk feet for screening purposes.

Results: Participants had a mean age of 63.1 ± 12.6 years (52.3% female), and 62.1% had been diagnosed with diabetes for more than 10 years. The average body mass index was 27.5 ± 5.6 kg/m2. Of the thermal images, 91 were classified as category 0 and 62 as categories 1 to 3, according to the diabetic foot risk classification system of the International Working Group on the Diabetic Foot. Using five-fold cross-validation, the neural network model achieved an overall accuracy of 71.8 ± 4.9%, a sensitivity of 81.2 ± 10.0%, and a specificity of 64.0 ± 7.4%. Additionally, the Matthews correlation coefficient was 0.46 ± 0.08.

Conclusions: These results suggest that thermography combined with deep learning could be developed for screening purposes to stratify patients at risk of developing diabetic foot ulcers.
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NewsBot · Jan 4, 2026

High-Accuracy Diabetic Foot Prediction Using Thermal Imaging and KNN-Based Machine Learning
Ahmed Akeel Ali, Sadik Kamel Gharghan & Adnan Hussein Ali
Sensing and Imaging

Diabetic Foot (DF) is a complication of diabetes, a condition that affects the feet as a result of poor circulation and peripheral nerve disorders due to long-term high blood sugar. Failure to detect DF early causes serious problems, such as amputation of the lower limbs if it is not treated initially. To avoid the consequences of this disease, real-time diagnosis of DF is crucial. This paper aims to predict diabetic feet in real time using thermal visualization and Machine Learning (ML) algorithms that detect abnormal changes in skin temperature. Thus, it can avoid serious complications caused by DF. Three ML algorithms, including Support Vector Machine)SVM(, Random Forest)RF(, and K-Nearest Neighbors (KNN), and one deep learning model, Visual Geometry Group 16-layer network (VGG16), implemented in Python to classify and predict healthy/unhealthy feet using feature extraction from foot thermal images. Different thermal imaging data from 112 individuals (95 from Kaggle and 17 from Endocrine Center in Baghdad) were collected, which amounted to 2,240 thermal images, equally divided into healthy and unhealthy. 80% of the dataset was used to train, and 20% for testing the ML algorithm. The algorithms were evaluated regarding several performance metrics, such as accuracy, sensitivity, specificity, precision, F1-Score, and Area Under the Curve (AUC). The KNN algorithm outperformed other ML algorithms used in this research and those used in previous research in terms of accuracy, reaching 99.47% compared to the other algorithms used in this research and those used in previous research. The use of ML algorithms with thermal imaging shows high success in detecting temperature variations and identifying early-stage complications of DF.
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NewsBot · Mar 6, 2026

Infrared Thermography in Diabetic Foot Assessment: Review
Thelma I. Morales-Ramírez et al
Diabetology 2026, 7(3), 47

One of the most common and severe complications of diabetes mellitus is diabetic foot, making early detection a public health priority. Infrared thermography is a promising noninvasive technique for identifying abnormal thermal patterns associated with inflammation, neuropathy, angiopathy, and tissue damage. This technique involves acquiring infrared radiation emitted by the skin and processing it to generate thermal maps that reflect underlying physiological changes. However, the reliability of thermographic assessments depends on strict technical conditions, including sensor performance, environmental control, and reproducible measurements. Despite its advantages, the clinical adoption of thermography is limited by the absence of standardized acquisition protocols and the influence of external and physiological factors on temperature measurements. Addressing these challenges is essential to ensure the accurate interpretation and validation of results. Recent advances, such as the incorporation of artificial intelligence algorithms and the development of portable, low-cost devices, offer new opportunities to enhance thermography’s applicability in clinical settings and home monitoring
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NewsBot · Apr 5, 2026 at 1:28 AM

Improving Diabetic Foot Care With Infrared Thermography and Artificial Intelligence: A Review
Pedro Teixeira et al
J Diabetes Sci Technol. 2026 Apr 3:19322968261432639.

Background: One of the most common consequences in individuals with diabetes is the diabetic foot, which can cause foot ulcers and even lead to limb amputation. Since an increase of the temperature in the plantar region is directly correlated with an increased risk of ulceration, infrared thermography (IRT) has been used in multiple studies as an automatic tool for detecting problems in diabetic foot. Artificial intelligence-based computer-aided diagnosis systems are being more frequently used to improve decision-making and minimize errors. These technologies are designed to increase examination accuracy, consistency in image interpretation, prognosis evaluation support, and examination accuracy. They also have the ability to offer insightful information and help medical professionals to manage diabetic foot issues successfully.

Methods: In this work, 37 papers that used thermography and artificial intelligence (AI) to identify diabetic foot complications and/or predict the risk of developing diabetic foot are analyzed.

Results: The results demonstrate the potential of IRT imaging implementation with AI for the identification and prediction of diabetic foot complications.

Conclusions: The combination of IRT and AI shows significant potential for diabetic foot assessment; however, the great majority of these studies show that the research is confined to classification of foot thermograms using pre-prepared data sets. In particular, there is limited research on segmentation methods and constraints in the use of deep learning due to the lack of large and diverse datasets.
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Home Monitoring of Foot Skin Temperatures to Prevent Ulceration

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Diabetic foot ulcer prevention by home monitoring of temperature

Smart insole for continuous monitoring of frail people at home

How to Measure Your Own Leg Length Discrepancy at Home

work-from-home policies and interest in foot surgery

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Home Monitoring of Foot Skin Temperatures to Prevent Ulceration

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Diabetic foot ulcer prevention by home monitoring of temperature

Smart insole for continuous monitoring of frail people at home

How to Measure Your Own Leg Length Discrepancy at Home

work-from-home policies and interest in foot surgery

Homeopathy for Diabetic Foot Ulcers

Podiatrist wanted for home visit round in Wigan area

contracting for nursing home help!

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