Anabelle Mizzi

This study aimed to establish normative in-shoe plantar foot temperature patterns across three static postures—lying, sitting, and standing—in healthy individuals, providing a clinically relevant baseline for interpreting in-shoe thermograms in diabetic or peripheral arterial disease (PAD) populations. A single-center prospective study included 20 healthy adults (40 limbs; 22–74 years) who underwent vascular and neurological screening prior to data collection. Plantar temperature was continuously recorded using Tarsos^® Smart Insoles with 21 embedded sensors per foot during three consecutive 10 min phases: supine, sitting, and standing. Data were analyzed for regional differences across the toes, metatarsals, arch, and heel using statistical and visual methods. Distinct posture-related temperature patterns were observed. The arch consistently exhibited the highest temperatures, while the toes remained cooler across all phases. Supine positioning resulted in relatively uniform temperature increases, whereas sitting and standing demonstrated more-stable but region-specific patterns, with slower rates of temperature change and more pronounced regional variation. Compared with barefoot thermography, the in-shoe condition showed greater heat retention and reduced evaporative cooling, highlighting the importance of context-specific baseline data. These findings demonstrate the influence of posture on plantar thermal distribution in the in-shoe environment and support the use of embedded monitoring systems for continuous assessment where surface thermography is not feasible.

Monitoring plantar foot temperatures is essential for assessing foot health, particularly in individuals with diabetes at increased risk of complications. Traditional thermographic imaging measures foot temperatures in unshod individuals lying down, which may not reflect thermal characteristics of feet in shod, active, real-world conditions. These controlled settings limit understanding of dynamic foot temperatures during daily activities. Recent advancements in wearable technology, such as insole-based sensors, overcome these limitations by enabling continuous temperature monitoring. This study leverages a data-driven clustering approach, independent of pre-selected foot regions or models like the angiosome concept, to explore normative thermal patterns in shod feet with insole-based sensors. Data were collected from 27 healthy participants using insoles embedded with 21 temperature sensors. The data were analysed using clustering algorithms, including k-means, fuzzy c-means, OPTICS, and hierarchical clustering. The clustering algorithms showed a high degree of similarity, with variations primarily influenced by clustering granularity. Six primary thermal patterns were identified, with the “butterfly pattern” (elevated medial arch temperatures) predominant, representing 51.5% of the dataset, aligning with findings in thermographic studies. Other patterns, like the “medial arch + metatarsal area” pattern, were also observed, highlighting diverse yet consistent thermal distributions. This study shows that while normative thermal patterns observed in thermographic imaging are reflected in insole data, the temperature distribution within the shoe may better represent foot behaviour during everyday activities, particularly when enclosed in a shoe. Unlike thermal imaging, the proposed in-shoe system offers the potential to capture dynamic thermal variations during ambulatory activities, enabling richer insights into foot health in real-world conditions.

In this work, we describe the use of time series features extracted from foot temperature data obtained from a wearable in-shoe system to discriminate between feet from individuals affected by diabetes, and feet from healthy individuals. We identify a set of features that are statistically significant in discriminating between the two classes and that thus can serve as input to machine learning classifiers.

The aim of this study was to determine the lower-limb outcome in patients with intermittent claudication (IC) and to identify predictors for deterioration. This study employed a prospective observational cohort single-centre design. One hundred fifty patients with IC attending a vascular surgery unit for the first time were recruited. Lower limb perfusion was assessed utilising ankle brachial index (ABI) measures, toe-brachial index (TBI) measures, Doppler waveform analysis and the walking impairment questionnaire. Follow-up was conducted after 1 year and 2 years following recruitment to assess haemodynamic parameters, symptom severity and outcome. Recruited participants had a mean age of 69.7 (±9.3) years, BMI 27.8(±4.2) and 79.3% were men. Significant haemodynamic decline (decline in ABPI by ≥0.15 and/or decline in TBPI by ≥0.1) occurred in 50.6% of the cohort within 2 years of whom 23.3% developed chronic limb threatening ischaemia (CLTI) with rest pain and/or tissue loss. Baseline ABPI, ABPI ≤ 0.5, TBPI ≤ 0.39, infrapopliteal artery (IPA) disease and high Haemoglobin A1c were identified as significant predictors for deterioration to CLI. ( P < .05, binomial logistic regression). Patients with IC are at a high risk of developing CLTI within 2 years. Risk of lower limb adverse events is tripled in patients with IPA disease, low ankle and toe pressures and poorly controlled diabetes. Early identification of those at high risk for early deterioration may justify a paradigm shift in the management of this subgroup.

Background: Intermittent claudication (IC) is the most common symptom of peripheral arterial disease and is generally treated conservatively due to limited prognostic evidence to support early revascularisation in the individual patient. This approach may lead to the possible loss of opportunity of early revascularisation in patients who are more likely to deteriorate to critical limb ischaemia. The aim of this review is to evaluate the available literature related to the progression rate of symptomatic peripheral arterial disease.Methods: We conducted a systematic review of the literature in PubMed and MEDLINE, Cochrane library, Elsevier, Web of Science, CINAHL and Opengrey using relevant search terms to identify the progression rate of peripheral arterial disease in patients with claudication. Outcomes of interest were progression rate in terms of haemodynamic measurement and time to development of adverse outcomes. Two independent reviewers determined study eligibility and extracted descriptive, methodologic, and outcome data. Quality of evidence was evaluated using the Cochrane recommendations for assessing risk of bias and was reported according to the Preferred Reporting Items for Systematic Reviews and Meta‐Analyses guidelines.Results: Seven prospective cohort studies and one retrospective cohort study were identified and included in this review with the number of participants in each study ranging from 38 to 1244. Progression rate reports varied from a yearly decrease of 0.01 in ankle‐brachial pressure index (ABPI) to a yearly decrease ABPI of 0.014 in 21% of participants. Quality of evidence ranged from low to moderate mostly due to limited allocation concealment at recruitment and survival selection bias.Conclusions: Progression of PAD in IC patients is probably underestimated in the literature due to study design issues. Predicting which patients with claudication are likely to deteriorate to critical limb ischaemia is difficult since there is a lack of evidence related to lower limb prognosis. Further research is required to enable early identification of patients at high risk of progressing to critical ischaemia and appropriate early revascularisation to reduce lower limb morbidity.

This work develops a method for automatically extracting temperature data from prespecified anatomical regions of interest from thermal images of human hands, feet, and shins for the monitoring of peripheral arterial disease in diabetic patients. Binarisation, morphological operations, and geometric transformations are applied in cascade to automatically extract the required data from 44 predefined regions of interest. The implemented algorithms for region extraction were tested on data from 395 participants. A correct extraction in around 90% of the images was achieved. The process of automatically extracting 44 regions of interest was performed in a total computation time of approximately 1 minute, a substantial improvement over 10 minutes it took for a corresponding manual extraction of the regions by a trained individual. Interrater reliability tests showed that the automatically extracted ROIs are similar to those extracted by humans with minimal temperature difference. This set of algorithms provides a sufficiently accurate and reliable method for temperature extraction from thermal images at par with human raters with a tenfold reduction in time requirement. The automated process may replace the manual human extraction, leading to a faster process, making it feasible to carry out large-scale studies and to increase the regions of interest with minimal cost. The code for the developed algorithms, to extract the 44 ROIs from thermal images of hands, feet, and shins, has been made available online in the form of MATLAB functions and can be accessed from http://www.um.edu.mt/cbc/tipmid.

Thermal imaging can provide an image of the surface temperature of an object in a non-contact and noninvasive manner, making it particularly appealing for use in medical applications. In applications where it is desirable to extract temperature data from anatomical regions of interest (ROIs) in a standardised and consistent manner, the use of automated segmentation and analysis techniques can provide a faster, more reliable and more consistent approach than manual segmentation of these ROIs. In this paper we present an algorithm which automatically extracts temperature data from eight ROIs in thermal images of the volar aspect of human hands. The algorithm first identifies the hand from the background in the thermal image and then identifies pixels which make up the fingers and the palm. Finally, eight ROIs are extracted from the identified regions. The methods proposed in this work can also be extended for the processing of similar visual images.

Objectives. To collect normative baseline data and identify any significant differences between hand and foot thermographic distribution patterns in a healthy adult population.Design. A single-centre, randomized, prospective study.Methods. Thermographic data was acquired using a FLIR camera for the data acquisition of both plantar and dorsal aspects of the feet, volar aspects of the hands, and anterior aspects of the lower limbs under controlled climate conditions.Results. There is general symmetry in skin temperature between the same regions in contralateral limbs, in terms of both magnitude and pattern. There was also minimal intersubject temperature variation with a consistent temperature pattern in toes and fingers. The thumb is the warmest digit with the temperature falling gradually between the 2nd and the 5th fingers. The big toe and the 5th toe are the warmest digits with the 2nd to the 4th toes being cooler.Conclusion. Measurement of skin temperature of the limbs using a thermal camera is feasible and reproducible. Temperature patterns in fingers and toes are consistent with similar temperatures in contralateral limbs in healthy subjects. This study provides the basis for further research to assess the clinical usefulness of thermography in the diagnosis of vascular insufficiency.