Automated Region Extraction from Thermal Images for Peripheral Vascular Disease Monitoring

Evaluation of Thermography as a Diagnostic Technique in Asymptomatic or Incipient Onychomycosis

Onychomycosis is usually diagnosed symptomatically due to the very clear signs caused by the fungus on the nail surface and structure, although the growth of the infecting agent must also be verified by culture in an enriched medium. This procedure is normally lengthy (four weeks), and samples can be contaminated, delaying the prescription of appropriate and effective treatment. Only one previous study has addressed the possibility of using thermography as a diagnostic method for onychomycosis in older people (31–70 years). The present study confirms this use but in individuals aged 18–31 years with incipient mycosis and no pathological signs. Using an FLIR E60 BX camera in a study with 214 samples, we found that men had more onychomycosis than women. We observed a relation between the presence of infection and nail temperature, with a higher temperature in yeast infections (+1 °C) and a lower temperature in dermatophyte infections (−2 °C). A higher temperature by almost 1 °C was also observed in older participants. Thermography can be viewed as a new diagnostic method in asymptomatic or incipient onychomycosis, providing the thermographic camera is sufficiently sensitive and the appropriate procedure is followed, although fungal culture is always necessary to confirm recovery after treatment.

Application of machine learning algorithms in thermal images for an automatic classification of lumbar sympathetic blocks

PURPOSE

There are no previous studies developing machine learning algorithms in the classification of lumbar sympathetic blocks (LSBs) performance using infrared thermography data. The objective was to assess the performance of different machine learning algorithms to classify LSBs carried out in patients diagnosed with lower limbs Complex Regional Pain Syndrome as successful or failed based on the evaluation of thermal predictors.

METHODS

66 LSBs previously performed and classified by the medical team were evaluated in 24 patients. 11 regions of interest on each plantar foot were selected within the thermal images acquired in the clinical setting. From every region of interest, different thermal predictors were extracted and analysed in three different moments (minutes 4, 5, and 6) along with the baseline time (just after the injection of a local anaesthetic around the sympathetic ganglia). Among them, the thermal variation of the ipsilateral foot and the thermal asymmetry variation between feet at each minute assessed and the starting time for each region of interest, were fed into 4 different machine learning classifiers: an Artificial Neuronal Network, K-Nearest Neighbours, Random Forest, and a Support Vector Machine.

RESULTS

All classifiers presented an accuracy and specificity higher than 70%, sensitivity higher than 67%, and AUC higher than 0.73, and the Artificial Neuronal Network classifier performed the best with a maximum accuracy of 88%, sensitivity of 100%, specificity of 84% and AUC of 0.92, using 3 predictors.

CONCLUSION

These results suggest thermal data retrieved from plantar feet combined with a machine learning-based methodology can be an effective tool to automatically classify LSBs performance.

Determination of Changes in Tissue Perfusion at Home with Hyperspectral and Thermal Imaging in the First Six Weeks after Endovascular Therapy in Patients with Peripheral Arterial Disease

The aims of this study were to assess changes in tissue perfusion up to 6 weeks after endovascular therapy (EVT), in hospital and at home, and to determine differences in tissue perfusion between patients with and without clinical improvement or good angiographic result. This single-center prospective cohort study included patients undergoing EVT for Rutherford stages two to six. Hyperspectral and thermal imaging were performed at the dorsal and plantar sides of the foot. These measurements consisted of a baseline measurement pre-EVT, and six follow-up measurements obtained at 1 and 4 h and 6 weeks in hospital, and 1 day, 7 days, and 14 days at home. Clinical improvement was defined as a decrease of one or more Rutherford class or decrease in the wound surface area and a good angiographic result was accomplished when a Transatlantic Inter-Society Consensus for the Management of PAD II C or D lesion was treated and uninterrupted flow continued in at least one below-the-knee artery in continuation with the inframalleolar arteries. The study included 34 patients with 41 treated limbs. Deoxyhemoglobin values were lower 1 h post-EVT compared with baseline and increased over time up to 6 weeks post-EVT. Significant differences in deoxyhemoglobin levels at 7 and 14 days post-EVT were determined between patients with and without clinical or angiographic success. This prospective pilot study shows the feasibility of hyperspectral imaging and thermal imaging post-EVT at home, which may decrease the need for hospital visits.

Skin Temperature Assessment During Lumbar Sympathetic Blocks by Infrared Thermography

Complex Regional Pain Syndrome (CRPS) is a pain disorder that can be triggered by injuries or surgery affecting most often limbs. Its multifaceted pathophysiology makes its diagnosis and treatment a challenging work. To reduce pain, patients diagnosed with CRPS commonly undergo sympathetic blocks which involves the injection of a local anesthetic drug around the nerves. Currently, this procedure is guided by fluoroscopy which occasionally is considered as little accurate. For this reason, the use of infrared thermography as a technique of support has been considered.In this work, thermal images of feet soles in patients with lower limbs CRPS undergoing lumbar sympathetic blocks were recorded and evaluated. The images were analyzed by means of a computer-aided intuitive software tool developed using MATLAB. This tool provides the possibility of editing regions of interest, extracting the most important information of these regions and exporting the results data to an Excel file.Clinical Relevance- The final purpose of this work is to value the potential of infrared thermography and the analysis of its images as an intraoperatory technique of support in lumbar sympathetic blocks in patients with lower limbs CRPS.

Quantitative Analysis of Real-Time Infrared Thermography for the Assessment of Lumbar Sympathetic Blocks: A Preliminary Study

Lumbar sympathetic blocks (LSBs) are commonly performed to treat pain ailments in the lower limbs. LSBs involve injecting local anesthetic around the nerves. The injection is guided by fluoroscopy which is sometimes considered to be insufficiently accurate. The main aim was to analyze the plantar foot skin temperature data acquired while performing LSBs in patients with complex regional pain syndrome (CRPS) affecting the lower limbs. Forty-four LSBs for treating lower limb CRPS in 13 patients were assessed. Pain medicine physicians visualized the infrared thermography (IRT) video in real time and classified the performance depending on the observed thermal changes within the first 4 min. Thirty-two percent of the cases did not register temperature variations after lidocaine was injected, requiring the needle to be relocated. Differences between moments are indicated using the 95% confidence intervals of the differences (CI 95%), the Cohen effect size (ES) and the significance (p value). In successful cases, after injecting lidocaine, increases at minute 7 for the mean (CI 95% (1.4, 2.1 °C), p < 0.001 and ES = 0.5), at minute 5 for maximum temperature (CI 95% (2.3, 3.3 °C), p < 0.001 and ES = 0.6) and at minute 6 for SD (CI 95% (0.2, 0.3 °C), p < 0.001 and ES = 0.5) were observed. The results of our preliminary study showed that the measurement of skin temperature in real time by infrared thermography is valuable for assessing the success of lumbar sympathetic blocks.

Framework for Real-Time Detection and Identification of possible patients of COVID-19 at public places

The novel Corona Virus (COVID-19) has become the reason for the world to declare it as a global pandemic, which has already taken many lives from all around the world. This pandemic has become a disaster since the spreading rate from person to person is incredibly high and many techniques have come forth to aid in stopping the infection. Although various types of methods have been put into implementation, the search and suggestions of new approaches to reduce the increasing rate of infection will never come to an end until a vaccine terminates this pandemic. This study focuses on proposing a new framework that is based on Deep Learning algorithms for recognizing the COVID-19 cases, mostly in public places. The algorithms include Background Subtraction for extracting the foreground of thermal images from thermal videos generated by Thermal Cameras through the Thermal Imaging process and the Convolutional Neural Network for detecting people infected with the virus. This automated prototype works in a real-time scenario that helps identify people with the disease and will try to trace it while separating them from having any other contact. This proposal intends to achieve a satisfying growth in determining the real cases of COVID-19 and minimize the spreading rate of this virus to the max, ultimately avoiding more deaths.

Influence of infrared camera model and evaluator reproducibility in the assessment of skin temperature responses to physical exercise

Infrared thermography (IRT) has been gaining in popularity in clinical and scientific research due to the increasing availability of affordable infrared cameras. This study aims to determine the similarity of measurement performance between three models of IRT camera during assessment of skin temperature before and after physical exercise. Three models of FLIR thermographic cameras (E60bx, Flir-One Pro LT, and C2) were tested. Thermal images were taken of the foot sole, anterior leg, and anterior thigh from 12 well-trained men, before and after a 30-min run on a treadmill. Image files were blinded and processed by three evaluators to extract the mean, maximum, and standard deviation of skin temperature of the region of interest. Time for data processing and rate of perceived effort was also recorded. Data processing was slower on the E60bx (CI95% E60 vs C2 [0.2, 2.6 min], p = 0.02 and ES = 0.6); vs. Flir-One [0.0, 3.4 min], p = 0.03 and ES = 0.6) and was associated with lower effort perception (E60 3.0 ± 0.1 vs. Flir-One 5.6 ± 0.2 vs C2 7.0 ± 0.2 points; p < 0.001 and ES > 0.8). The C2 and Flir-One cameras underestimated the temperature compared with the E60. In general, measuring mean temperature provided higher camera and examiner intra-class correlations than maximum and standard deviation, especially before exercise. Moreover, post exercise mean skin temperatures provided the most consistent values across cameras and evaluators. We recommend the use of mean temperature and caution when using more than one camera model in a study.

Non-Contact Assessment of Peripheral Artery Haemodynamics Using Infrared Video Thermography

Skin temperature has long been used as a natural indicator of vascular diseases in the extremities. Considerable correlation between oscillations in skin surface temperature and oscillations of skin blood flow has previously been demonstrated. We hypothesised that the impairment of blood flow in stenotic (subcutaneous) peripheral arteries would influence cutaneous temperature such that, by measuring gradients in the temperature distribution over skin surfaces, one may be able to diagnose or quantify the progression of vascular conditions in whose pathogenesis a reduction in subcutaneous blood perfusion plays a critical role (e.g. peripheral artery disease). As proof of principle, this study investigates the local changes in the skin temperature of healthy humans (15 male, [Formula: see text] years old, BMI [Formula: see text] kg/m ²) undergoing two physical challenges designed to vary their haemodynamic status. Skin temperature was measured in four central regions (forehead, neck, chest, and left shoulder) and four peripheral regions (left upper arm, forearm, wrist, and hand) using an infrared thermal camera. We compare inter-region patterns. Median temperature over the peripheral regions decreased from baseline after both challenges (maximum decrease: [Formula: see text] °C at 60 s after exercise; [Formula: see text] and [Formula: see text] °C at 180 s of cold-water immersion; [Formula: see text]). Median temperature over the central regions showed no significant changes. Our results show that the non-contact measurement of perfusion-related changes in peripheral temperature from infrared video data is feasible. Further research will be directed towards the thermographic study of patients with symptomatic peripheral vascular disease.