Computer aided diagnosis of diabetic foot using infrared thermography: A review

Infrared thermography in clinical practice: a literature review

BACKGROUND

Infrared thermography technology is a diagnostic imaging modality that converts temperature information on the surface of the human body into visualised thermograms. This technology has the capacity to intuitively detect the presence of certain abnormal conditions or foci in the human body. In recent years, the application of this technology in medicine has become increasingly extensive, especially in the areas of auxiliary diagnosis and early screening of diseases.

OBJECTIVES

The aim of this review is to analyse and summarise the application of infrared thermography in clinical practice.

METHODS

A comprehensive search of the research literature pertaining to the clinical application of medical infrared thermography was conducted, encompassing publications by both domestic and foreign researchers and scholars, in prominent databases including PubMed, ISI Web of Science, and CNKI since the inception of these databases.

RESULTS

A total of 51 articles were ultimately included in the study. The application of infrared thermography has been demonstrated in oncology, painful diseases, inflammation, rheumatism, and vascular-related diseases.

CONCLUSIONS

The extensive utilisation of infrared thermography in clinical settings signifies the technology's considerable potential. Addressing its current limitations can optimise its benefits.

Advances in Machine Learning-Aided Thermal Imaging for Early Detection of Diabetic Foot Ulcers: A Review

The prevention and early warning of foot ulcers are crucial in diabetic care; however, early microvascular lesions are difficult to detect and often diagnosed at later stages, posing serious health risks. Infrared thermal imaging, as a rapid and non-contact clinical examination technology, can sensitively detect hidden neuropathy and vascular lesions for early intervention. This review provides an informative summary of the background, mechanisms, thermal image datasets, and processing techniques used in thermal imaging for warning of diabetic foot ulcers. It specifically focuses on two-dimensional signal processing methods and the evaluation of computer-aided diagnostic methods commonly used for diabetic foot ulcers.

Can thermography predict diabetic foot ulcer risk in patients with diabetes mellitus? A systematic review

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.

DEC-DRR: deep ensemble of classification model for diabetic retinopathy recognition

Most diabetes patients are liable to have diabetic retinopathy (DR); however, the majority of them might not be even aware of the ailment. Therefore, early detection and treatment of DR are necessary to prevent vision loss. But, avoiding DR is not a simple process. An ophthalmologist can typically identify DR through an optical evaluation of the fundus and through the evaluation of color pictures. However, due to the increased count of DR patients, this could not be possible as it consumes more time. To rectify this problem, a novel deep ensemble-based DR classification technique is developed in this work. Initially, a Wiener filter (WF) is applied for preprocessing the image. Then, the enhanced U-Net-based segmentation process is done. Subsequent to the segmentation process, features are extracted that include statistical features, inferior superior nasal temporal (ISNT), cup to disc ratio (CDR), and improved LGBP as well. Further, deep ensemble classifiers (DEC) like CNN, Bi-GRU, and DMN are used to recognize the disease. The outcomes from DMN, CNN, and Bi-GRU are then subjected to improved SLF. Additionally, the weights of DMN, CNN, and Bi-GRU are adjusted via pelican updated Tasmanian devil optimization (PU-TDO). Finally, outputs on DR (microaneurysms, hemorrhages, hard exudates, and soft exudates) are obtained. The performance of DEC + PU-TDO for diabetic retinopathy is computed over extant models with regard to different measures for four datasets. The results on accuracy using the DEC + PU-TDO scheme for the IDRID dataset is maximum around 0.975 at 90th LP while other models have less accuracy. The FPR of DEC + PU-TDO is less around 0.039 at the 90th LP for the SUSTech-SYSU dataset, while other extant models have maximum FPR.

The diagnostic accuracy of infrared thermography in lumbosacral radicular pain: a prospective study

BACKGROUND

To identify the sensitivity, specificity, and overall diagnostic accuracy of infrared thermography in diagnosing lumbosacral radicular pain.

METHODS

Patients sequentially presenting with lower extremity pain were enrolled. A clinical certainty score ranging from 0 to 10 was used to assess the likelihood of lumbosacral radicular pain, with higher scores indicating higher likelihood. Infrared Thermography scans were performed and the temperature difference (ΔT) was calculated as ΔT = T1 - T2, where T2 represents the skin temperature of the most painful area on the affected limb and T1 represents the skin temperature of the same area on the unaffected limb. Upon discharge from the hospital, two independent doctors diagnosed lumbosacral radicular pain based on intraoperative findings, surgical effectiveness, and medical records.

RESULTS

A total of 162 patients were included in the study, with the adjudicated golden standard diagnosis revealing that 101 (62%) patients had lumbosacral radicular pain, while the lower extremity pain in 61 patients was attributed to other diseases. The optimal diagnostic value for ΔT was identified to fall between 0.8℃ and 2.2℃, with a corresponding diagnostic accuracy, sensitivity, and specificity of 80%, 89%, and 66% respectively. The diagnostic accuracy, sensitivity, and specificity for the clinical certainty score were reported as 69%, 62%, and 79% respectively. Combining the clinical certainty score with ΔT yielded a diagnostic accuracy, sensitivity, and specificity of 84%, 77%, and 88% respectively.

CONCLUSION

Infrared thermography proves to be a highly sensitive tool for diagnosing lumbosacral radicular pain. It offers additional diagnostic value in cases where general clinical evaluation may not provide conclusive results.

TRIAL REGISTRATION

ChiCTR2300078786, 19/22/2023.

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

Background

Diabetic foot complications impose a significant strain on healthcare systems worldwide, acting as a principal cause of morbidity and mortality in individuals with diabetes mellitus. While traditional methods in diagnosing and treating these conditions have faced limitations, the emergence of Machine Learning (ML) technologies heralds a new era, offering the promise of revolutionizing diabetic foot care through enhanced precision and tailored treatment strategies.

Objective

This review aims to explore the transformative impact of ML on managing diabetic foot complications, highlighting its potential to advance diagnostic accuracy and therapeutic approaches by leveraging developments in medical imaging, biomarker detection, and clinical biomechanics.

Methods

A meticulous literature search was executed across PubMed, Scopus, and Google Scholar databases to identify pertinent articles published up to March 2024. The search strategy was carefully crafted, employing a combination of keywords such as "Machine Learning," "Diabetic Foot," "Diabetic Foot Ulcers," "Diabetic Foot Care," "Artificial Intelligence," and "Predictive Modeling." This review offers an in-depth analysis of the foundational principles and algorithms that constitute ML, placing a special emphasis on their relevance to the medical sciences, particularly within the specialized domain of diabetic foot pathology. Through the incorporation of illustrative case studies and schematic diagrams, the review endeavors to elucidate the intricate computational methodologies involved.

Results

ML has proven to be invaluable in deriving critical insights from complex datasets, enhancing both the diagnostic precision and therapeutic planning for diabetic foot management. This review highlights the efficacy of ML in clinical decision-making, underscored by comparative analyses of ML algorithms in prognostic assessments and diagnostic applications within diabetic foot care.

Conclusion

The review culminates in a prospective assessment of the trajectory of ML applications in the realm of diabetic foot care. We believe that despite challenges such as computational limitations and ethical considerations, ML remains at the forefront of revolutionizing treatment paradigms for the management of diabetic foot complications that are globally applicable and precision-oriented. This technological evolution heralds unprecedented possibilities for treatment and opportunities for enhancing patient care.

State-of-the-Art Features for Early-Stage Detection of Diabetic Foot Ulcers Based on Thermograms

Diabetic foot ulcers represent the most frequently recognized and highest risk factor among patients affected by diabetes mellitus. The associated recurrent rate is high, and amputation of the foot or lower limb is often required due to infection. Analysis of infrared thermograms covering the entire plantar aspect of both feet is considered an emerging area of research focused on identifying at an early stage the underlying conditions that sustain skin and tissue damage prior to the onset of superficial wounds. The identification of foot disorders at an early stage using thermography requires establishing a subset of relevant features to reduce decision variability and data misinterpretation and provide a better overall cost-performance for classification. The lack of standardization among thermograms as well as the unbalanced datasets towards diabetic cases hinder the establishment of this suitable subset of features. To date, most studies published are mainly based on the exploitation of the publicly available INAOE dataset, which is composed of thermogram images of healthy and diabetic subjects. However, a recently released dataset, STANDUP, provided data for extending the current state of the art. In this work, an extended and more generalized dataset was employed. A comparison was performed between the more relevant and robust features, previously extracted from the INAOE dataset, with the features extracted from the extended dataset. These features were obtained through state-of-the-art methodologies, including two classical approaches, lasso and random forest, and two variational deep learning-based methods. The extracted features were used as an input to a support vector machine classifier to distinguish between diabetic and healthy subjects. The performance metrics employed confirmed the effectiveness of both the methodology and the state-of-the-art features subsequently extracted. Most importantly, their performance was also demonstrated when considering the generalization achieved through the integration of input datasets. Notably, features associated with the MCA and LPA angiosomes seemed the most relevant.

The application of infrared thermography technology in flap: A perspective from bibliometric and visual analysis

The application of infrared thermography technology (IRT) in flap has become a major focus of research, as it provides a non-invasive, real-time, and quantitative approach for monitoring flap perfusion. In this regard, we conducted a comprehensive visualization and scientometric analysis to systematically summarize and discuss the current state of research in this field. We systematically reviewed publications on the application of IRT in flap procedures from 1999 to 2022, using the Web of Science Core Collection (WoSCC). Through scientometric analysis, we examined annual trends, affiliations, countries, journals, authors, and their relationships, providing insights into current hotspots and future developments in this area. We analysed 522 English studies and found a steady increase in annual publications. The United States and Germany had the highest publication rates, with Beth Israel Deaconess Medical Center and Shanghai Jiaotong University being leading institutions. Notably, Lee BT and Alex Keller emerged as influential authors in this field. Compared to existing techniques, infrared-based technology offers significant advantages for non-invasive monitoring of flap perfusion, including simplicity of operation and objective results. Future trends should focus on interdisciplinary collaborations to develop new infrared devices and achieve intelligent image processing, enabling broader application in various clinical scenarios. This bibliometric study summarizes the progress and landscape of research on 'the Application of infrared thermography technology in flap' over the past two decades, providing valuable insights and serving as a reliable reference to drive further advancements and spark researchers' interest in this field.

Multi-Class Wound Classification via High and Low-Frequency Guidance Network

Wound image classification is a crucial preprocessing step to many intelligent medical systems, e.g., online diagnosis and smart medical. Recently, Convolutional Neural Network (CNN) has been widely applied to the classification of wound images and obtained promising performance to some extent. Unfortunately, it is still challenging to classify multiple wound types due to the complexity and variety of wound images. Existing CNNs usually extract high- and low-frequency features at the same convolutional layer, which inevitably causes information loss and further affects the accuracy of classification. To this end, we propose a novel High and Low-frequency Guidance Network (HLG-Net) for multi-class wound classification. To be specific, HLG-Net contains two branches: High-Frequency Network (HF-Net) and Low-Frequency Network (LF-Net). We employ pre-trained models ResNet and Res2Net as the feature backbone of the HF-Net, which makes the network capture the high-frequency details and texture information of wound images. To extract much low-frequency information, we utilize a Multi-Stream Dilation Convolution Residual Block (MSDCRB) as the backbone of the LF-Net. Moreover, a fusion module is proposed to fully explore informative features at the end of these two separate feature extraction branches, and obtain the final classification result. Extensive experiments demonstrate that HLG-Net can achieve maximum accuracy of 98.00%, 92.11%, and 82.61% in two-class, three-class, and four-class wound image classifications, respectively, which outperforms the previous state-of-the-art methods.

Artificial intelligence for automated detection of diabetic foot ulcers: A real-world proof-of-concept clinical evaluation

OBJECTIVE

Conduct a multicenter proof-of-concept clinical evaluation to assess the accuracy of an artificial intelligence system on a smartphone for automated detection of diabetic foot ulcers.

METHODS

The evaluation was undertaken with patients with diabetes (n = 81) from September 2020 to January 2021. A total of 203 foot photographs were collected using a smartphone, analysed using the artificial intelligence system, and compared against expert clinician judgement, with 162 images showing at least one ulcer, and 41 showing no ulcer. Sensitivity and specificity of the system against clinician decisions was determined and inter- and intra-rater reliability analysed.

RESULTS

Predictions/decisions made by the system showed excellent sensitivity (0.9157) and high specificity (0.8857). Merging of intersecting predictions improved specificity to 0.9243. High levels of inter- and intra-rater reliability for clinician agreement on the ability of the artificial intelligence system to detect diabetic foot ulcers was also demonstrated (Kα > 0.8000 for all studies, between and within raters).

CONCLUSIONS

We demonstrate highly accurate automated diabetic foot ulcer detection using an artificial intelligence system with a low-end smartphone. This is the first key stage in the creation of a fully automated diabetic foot ulcer detection and monitoring system, with these findings underpinning medical device development.

Diabetic Plantar Foot Segmentation in Active Thermography Using a Two-Stage Adaptive Gamma Transform and a Deep Neural Network

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.

Use of Infrared Thermometry to Observe Temperature Variation Associated with the Healing Process in Wounds and Ulcers: TIHUAP Cohort Study Protocol

We are interested in observing how temperature differences between the wound bed and perilesional skin are related to the healing process in primary care patients with wounds. Multisite prospective cohort study with one-year follow-up in the Metropolitan North area of Barcelona. Recruitment of patients over 18 years with an open wound will take place from January 2023 to September 2023. Temperature checks will be conducted on a weekly basis at control visits and wound care. The following variables will be measured: Percentage reduction of wound area over time, thermal index, the Kundin Wound Gauge, and the Resvech 2.0 Scale. The temperature will be measured weekly using a handheld thermometer and mesh grid to frame the temperature points. The healing trajectory will also be monitored on a monthly basis via photographic imaging, the Resvech Scale, calculation of wound size, percentage reduction of wound area over time, and thermal index for one year of follow-up or until the wound is cured. This study may represent a turning point for its introduction into primary care. Early diagnosis of wound complications would facilitate treatment decision-making for healthcare professionals, thus improving the management of resources related to chronic wounds.

Applicability of thermography for cancer diagnosis in small animals

Medical thermography is an imaging test used to monitor skin surface temperature. Although it is not a recent technique, significant advances have been made since the 2000s with the equipment modernization, leading to its popularization. In cancer diagnosis, the application of thermography is supported by the difference in thermal distribution between neoplastic processes and adjacent healthy tissue. The mechanisms involved in heat production by cancer cells include neoangiogenesis, increased metabolic rate, vasodilation, and the release of nitric oxide and pro-inflammatory substances. Currently, thermography has been widely studied in humans as a screening tool for skin and breast cancer, with positive results. In veterinary medicine, the technique has shown promise and has been described for skin and soft tissue tumors in felines, mammary gland tumors, osteosarcoma, mast cell tumors, and perianal tumors in dogs. This review discusses the fundamentals of the technique, monitoring conditions, and the role of thermography as a complementary diagnostic tool for cancer in veterinary medicine, as well as future perspectives for improvement.

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.

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

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

Diabetic wounds and artificial intelligence: A mini-review

Diabetic wound takes longer time to heal due to micro and macro-vascular ailment. This longer healing time can lead to infections and other health complications. Foot ulcers are one of the most common diabetic wounds. These are one of the leading cause of amputations. Medical science is continuously striving for improving quality of human life. A recent trend of amalgamation of knowledge, efforts and technological advancement of medical science experts and artificial intelligence researchers, has made tremendous success in diagnosis, prognosis and treatment of a variety of diseases. Diabetic wounds are no exception, as artificial intelligence experts are putting their research efforts to apply latest technological advancements in the field to help medical care personnel to deal with diabetic wounds in more effective manner. The presented study reviews the diagnostic and treatment research under the umbrella of Artificial Intelligence and computational science, for diabetic wound healing. Framework for diabetic wound assessment using artificial intelligence is presented. Moreover, this review is focused on existing and potential contribution of artificial intelligence to improve medical services for diabetic wound patients. The article also discusses the future directions for the betterment of the field that can lead to facilitate both, clinician and patients.

Infrared thermal imaging-based skin temperature response during cupping at two different negative pressures

Cupping therapy can relieve muscle fatigue and pain after exercise by increasing blood flow at the treatment site, which may lead to dynamic changes of the local skin temperature. This study aimed to analyze the effect of cupping on local skin temperature under two different negative pressures using infrared thermography (IRT). Cupping therapy was performed on the forearms of 22 healthy subjects using the negative pressures of − 0.03 and − 0.04 MPa. IRT was used to record the dynamic changes in skin temperature before, during, and after cupping. Both cupping pressures induced a non-linear skin temperature response: temperature decreased first and then increased during cupping, while it first increased and then decreased after cupping. A significant difference was noted between the two negative pressure groups in the maximum temperature increment after cupping (P < 0.001). Compared with the basal temperature before cupping, the maximum increase in skin temperature after cupping in the − 0.03 and − 0.04 MPa groups was 0.92 and 1.42 °C, respectively. The findings of this study can lay the foundation evaluating the curative effect of cupping based on IRT and provide an objective reference for selecting the cupping negative pressure.

A Deep Learning Method for Early Detection of Diabetic Foot Using Decision Fusion and Thermal Images

Diabetes mellitus (DM) is one of the major diseases that cause death worldwide and lead to complications of diabetic foot ulcers (DFU). Improper and late handling of a diabetic foot patient can result in an amputation of the patient’s foot. Early detection of DFU symptoms can be observed using thermal imaging with a computer-assisted classifier. Previous study of DFU detection using thermal image only achieved 97% of accuracy, and it has to be improved. This article proposes a novel framework for DFU classification based on thermal imaging using deep neural networks and decision fusion. Here, decision fusion combines the classification result from a parallel classifier. We used the convolutional neural network (CNN) model of ShuffleNet and MobileNetV2 as the baseline classifier. In developing the classifier model, firstly, the MobileNetV2 and ShuffleNet were trained using plantar thermogram datasets. Then, the classification results of those two models were fused using a novel decision fusion method to increase the accuracy rate. The proposed framework achieved 100% accuracy in classifying the DFU thermal images in binary classes of positive and negative cases. The accuracy of the proposed Decision Fusion (DF) was increased by about 3.4% from baseline ShuffleNet and MobileNetV2. Overall, the proposed framework outperformed in classifying the images compared with the state-of-the-art deep learning and the traditional machine-learning-based classifier.

Automatic Classification of Foot Thermograms Using Machine Learning Techniques

Diabetic foot is one of the main complications observed in diabetic patients; it is associated with the development of foot ulcers and can lead to amputation. In order to diagnose these complications, specialists have to analyze several factors. To aid their decisions and help prevent mistakes, the resort to computer-assisted diagnostic systems using artificial intelligence techniques is gradually increasing. In this paper, two different models for the classification of thermograms of the feet of diabetic and healthy individuals are proposed and compared. In order to detect and classify abnormal changes in the plantar temperature, machine learning algorithms are used in both models. In the first model, the foot thermograms are classified into four classes: healthy and three categories for diabetics. The second model has two stages: in the first stage, the foot is classified as belonging to a diabetic or healthy individual, while, in the second stage, a classification refinement is conducted, classifying diabetic foot into three classes of progressive severity. The results show that both proposed models proved to be efficient, allowing us to classify a foot thermogram as belonging to a healthy or diabetic individual, with the diabetic ones divided into three classes; however, when compared, Model 2 outperforms Model 1 and allows for a better performance classification concerning the healthy category and the first class of diabetic individuals. These results demonstrate that the proposed methodology can be a tool to aid medical diagnosis.

Differential Screening of Herniated Lumbar Discs Based on Bag of Visual Words Image Classification Using Digital Infrared Thermographic Images

Doctors in primary hospitals can obtain the impression of lumbosacral radiculopathy with a physical exam and need to acquire medical images, such as an expensive MRI, for diagnosis. Then, doctors will perform a foraminal root block to the target root for pain control. However, there was insufficient screening medical image examination for precise L5 and S1 lumbosacral radiculopathy, which is most prevalent in the clinical field. Therefore, to perform differential screening of L5 and S1 lumbosacral radiculopathy, the authors applied digital infrared thermographic images (DITI) to the machine learning (ML) algorithm, which is the bag of visual words method. DITI dataset included data from the healthy population and radiculopathy patients with herniated lumbar discs (HLDs) L4/5 and L5/S1. A total of 842 patients were enrolled and the dataset was split into a 7:3 ratio as the training algorithm and test dataset to evaluate model performance. The average accuracy was 0.72 and 0.67, the average precision was 0.71 and 0.77, the average recall was 0.69 and 0.74, and the F1 score was 0.70 and 0.75 for the training and test datasets. Application of the bag of visual words algorithm to DITI classification will aid in the differential screening of lumbosacral radiculopathy and increase the therapeutic effect of primary pain interventions with economical cost.

Diabetic foot thermal image segmentation using Double Encoder-ResUnet (DE-ResUnet)

The use of thermography in the early diagnosis of Diabetic Foot (DF) has proven its effectiveness in identifying areas of the plantar foot that are susceptible to ulcer development. Segmentation of the foot sole is one of the most pertinent technical issues that must be performed with great precision. However, because of the inherent difficulties of foot thermal images, such as unclarity and the existence of ambiguities, segmentation approaches have not demonstrated sufficiently accurate and reliable results for clinical use. In this study, we aim to develop a fully automated, robust and accurate segmentation of the diabetic foot. To this end, we propose a deep neural network architecture adopting the encoder-decoder concept called Double Encoder-ResUnet (DE-ResUnet). This network combines the strengths of residual network and U-Net architecture. Moreover, it takes advantage of RGB (Red, Green, Blue) colour images and fuses thermal and colour information to improve segmentation accuracy. Our database consists of 398 pairs of thermal and RGB images. The population includes two groups. The first group of 54 healthy subjects. And a second group of 145 diabetic patients from the National Hospital Dos de Mayo in Peru. The dataset is splitted into 50% for training, 25% for validation and the last 25% is used for testing. This proposed model provided robust and accurate automatic segmentations of the DF and outperformed other state of the art methods with an average intersection over union (IoU) of 97%. In addition, it is able to accurately delineate the part of toes and heels which are high risk regions for ulceration.

A modified classical-quantum model for diabetic foot ulcer classification

DFU is one of the most spreading diseases now day approximately more than one million patients suffer due to this disease. Undergo the procedure of removing their lower limb of the body due to the reason that they are not able enough to recognize this disease and get proper treatment from the doctors or physicians. Therefore, there is an urgent need of developing a Computer-Aided Design (CAD) system that can easily detect Diabetic Foot Ulcer (DFU). Therefore, in this study, a pre-trained ResNet-50 model and modified classical-quantum model are utilized for diabetic foot ulcer classification into corresponding classes such as normal/abnormal and ischaemia/non-ischaemia. The presented approach achieved classification accuracy is greater than 0.90 on abnormal/normal, ischaemia/non-ischaemia, and infection and non-infection foot images. The reported results depict that the proposed method outperformed as compared to recently published work in the domain of diabetic foot ulcers.

Automated Detection of Infection in Diabetic Foot Ulcer Images Using Convolutional Neural Network

A bacterial or bone infection in the feet causes diabetic foot infection (DFI), which results in reddish skin in the wound and surrounding area. DFI is the most prevalent and dangerous type of diabetic mellitus. It will mainly occur in people with heart disease, renal illness, or eye disease. The clinical signs and symptoms of local inflammation are used to diagnose diabetic foot infection. In assessing diabetic foot ulcers, the infection has significant clinical implications in predicting the likelihood of amputation. In this work, a diabetic foot infection network (DFINET) is proposed to assess infection and no infection from diabetic foot ulcer images. A DFINET consists of 22 layers with a unique parallel convolution layer with ReLU, a normalization layer, and a fully connected layer with a dropout connection. Experiments have shown that the DFINET, when combined with this technique and improved image augmentation, should yield promising results in infection recognition, with an accuracy of 91.98%, and a Matthews correlation coefficient of 0.84 on binary classification. Such enhancements to existing methods shows that the suggested approach can assist medical experts in automated detection of DFI.

Use of infrared thermography in medical diagnostics: a scoping review protocol

INTRODUCTION

Thermography offers a non-invasive radiation-free methodology for diagnostic imaging and temperature measurement, but the extent of the current application is unclear, as is the level of evidence for each use case. Moreover, population-based thermographic reference values for diagnostic purposes are nearly unknown. The aim of this scoping review is to identify patient populations and diseases in which thermography is applied, cataloguing of technical and environmental modalities, investigation of the existence of specific reference data and finally exploration of gaps and future tasks.

METHODS AND ANALYSIS

PubMed, Cochrane Database of Systematic Reviews and CENTRAL, Embase, Web of Science and OpenGrey are to be searched using pretested suitable search strategies, with no language restriction, but abstracts should be available in English or German and articles should not have been published before 2000. This limited time frame is due to the rapid technological progress, which makes it necessary to exclude reports based on outdated technology. The literature found will be selected on the basis of previously defined inclusion and exclusion criteria. Subsequently, relevant data will be extracted from the included references into a predesigned table. The selection and extraction process will be conducted by two researchers independently. The report of the results will be according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews checklist. The entire review process will follow the Joanna Briggs Institute approach. The scoping review protocol is registered at the Open Science Framework.

ETHICS AND DISSEMINATION

Ethical approval is not required for this work, but ethical medicine also obliges us to carefully consider diagnostic alternatives and compare them with current standards. The dissemination of the results will take place in a variety of ways. First and foremost through publication in an open access journal, but also through conference proceedings. In addition, this scoping review will serve to open up new research foci with regard to thermography.

Infrared thermography as an adjunctive tool for detection of femoral arterial thrombosis after cardiac catheterization: A prospective, pilot study

OBJECTIVES

To assess the utility of infrared thermography (IRT), to map skin temperature, in the detection of femoral arterial (FA) thrombosis after cardiac catheterization.

BACKGROUND

Ultrasound is a validated method for thrombus detection but is generally reserved as a confirmatory test for clinical suspicion due to various constraints.

METHODS

Prospective study of infants and children undergoing cardiac catheterization via FA access, comparing IRT and pulse examination. The thermograms, displayed in a color map with each pixel representing a temperature, were examined by qualitative assessment of symmetry in thermal patterns and quantitative image analysis with abnormal thermographic asymmetry defined as a difference of >10% between limbs.

RESULTS

In the 20 children enrolled, excellent agreement was found between the two methods with a Kappa value of 0.89. The median thermographic asymmetry in the nine children with pulse loss was 36 (13-76)%. Using receiver operating characteristic analysis, the asymmetrical pattern of ≥18% between limbs predicted the need for anticoagulation with a sensitivity of 100% and specificity of 89%. The area under the curve was 0.97 (95% confidence interval: 0.95-1). Children with absent pulse requiring anticoagulation showed a slower recovery in thermal asymmetry compared to those with a reduced pulse. By qualitative IRT assessment, all children with absent pulse requiring anticoagulation were correctly identified by 10 independent assessors.

CONCLUSIONS

This pilot study showed that IRT is feasible and reliable as an adjunctive tool for thrombus detection postcatheterization and treatment monitoring. Specific advantages of IRT include portability, affordability, and contactless image acquisition.

Thermal Analysis of Blood Flow Alterations in Human Hand and Foot Based on Vascular-Porous Media Model

Microvascular and Macrovascular diseases are serious complications of diabetic mellitus, which significantly affect the life quality of diabetic patients. Quantitative description of the relationship between temperature and blood flow is considerably important for non-invasive detection of blood vessel structural and functional lesions. In this study, thermal analysis has been employed to predict blood flow alterations in a foot and a cubic skin model successively by using a discrete vessel-porous media model and further compared the blood flows in 31 diabetic patients. The tissue is regarded as porous media whose liquid phase represents the blood flow in capillaries and solid phase refers to the tissue part. Discrete vascular segments composed of arteries, arterioles, veins, and venules were embedded in the foot model. In the foot thermal analysis, the temperature distributions with different inlet vascular stenosis were simulated. The local temperature area sensitive to the reduction of perfusion was obtained under different inlet blood flow conditions. The discrete vascular-porous media model was further applied in the assessment of the skin blood flow by coupling the measured skin temperatures of diabetic patients and an inverse method. In comparison with the estimated blood flows among the diabetic patients, delayed blood flow regulation was found in some of diabetic patients, implying that there may be some vascular disorders in these patients. The conclusion confirms the one in our previous experiment on diabetic rats. Most of the patients predicted to be with vascular disorders were diagnosed as vascular complication in clinical settings as well, suggesting the potential applications of the vascular-porous media model in health management of diabetic patients.

Quantitative assessment of Bell's palsy-related facial thermal asymmetry using infrared thermography: A preliminary study

The temperature distribution of normal human skin is symmetrical. Facial paralysis generally changes this thermal symmetry. The aim of this study is to analyze facial thermal asymmetry during the early onset of Bell's palsy, and to assess the feasibility of the diagnosis of early-onset Bell's palsy using infrared thermography (IRT). Fifteen subjects with Bell's palsy and 15 healthy volunteers were considered in this study. The infrared thermal images of the front, left, and right sides of all the subjects were collected and analyzed. Each group of facial thermograms was divided into 16 symmetrical regions of interest (ROIs) with respect to the left and right sides. Three different temperature difference calculation methods were used to express the degree of thermal symmetry between the left- and right-side ROIs, namely, the mean temperature difference (ΔT_roi), maximum temperature difference (ΔT_max), and minimum temperature difference (ΔT_min). Among the facial ROIs, there were significant differences in the thermal symmetries of the frontal region, medial canthus region, and infraorbital region between subjects with and without Bell's palsy (p < 0.05). Based on the results, ΔT_roi was more effective than the other two methods for the diagnosis of early-onset Bell's palsy. The area under the ROC curve (AUC) of ΔT_roi in the infraorbital region was 0.818; and the sensitivity and specificity were 0.867 and 0.800, respectively. Subjects with early-onset Bell's palsy exhibited thermal asymmetry on the left and right sides of their faces. The diagnosis of early-onset Bell's palsy using IRT is therefore necessary. Nevertheless, more effective thermal symmetry analysis methods will be investigated further in future research.

Prevention of diabetic foot ulcers using a smartphone and mobile thermography: a case study

OBJECTIVE

Early identification of pre-ulcerative pathology is important to preventing diabetic foot ulcers (DFU), but signs of inflammation are difficult to detect on the feet of patients with diabetic neuropathy due to decreased sensation. However, infrared thermography can objectively identify inflammation. Therefore, a device that allows patients to visualise thermograms of their feet might be an effective way to prevent DFU. We aimed to determine the effects of a novel self-monitoring device to prevent DFU using a thermograph attached to a smartphone.

METHOD

A self-monitoring device comprising a mobile thermograph attached to a smartphone on a selfie stick was created, and its effects in two patients with diabetic neuropathy and foot calluses assessed.

RESULTS

For one patient, he understood that walking too much increased the temperature in the skin of his feet (a sign of inflammation). The other patient could not detect high-risk findings, because the temperature of his skin did not increase during the study period.

CONCLUSION

This device might provide self-care incentives to prevent DFU, although some issues, such as the automatic detection of high-risk thermographic changes, need to be improved.

Thermography as a non-ionizing quantitative tool for diagnosing periapical inflammatory lesions

Background

Thermography is a contemporary imaging modality based on acquiring and analyzing thermal data using non-contact devices. The aim of the present study was to assess the validity of thermography, compared with that of the reference-standard, for the diagnosis of periapical inflammatory lesions and to evaluate the temperature ranges for acute pulpitis with apical periodontitis (AAP), acute periapical abscess (AA) and chronic periapical abscess (CA).

Methods

AAP, AA and CA were diagnosed based on clinical and radiographic criteria. Thermographic data were acquired using the FLIR E-5 Infrared Camera. Extraoral thermal images were taken from the front and right and left sides of patients whose mouths were closed, and one intraoral thermal image was taken from the palatal perspective. Agreement in the diagnoses based on the combination of clinical and radiographic assessments and the thermographic evaluation was calculated. The temperature ranges of the three diagnostic subgroups were also measured.

Results

A total of 80 patients were enrolled in this study. The mean intraoral thermal image temperature for AA was 37.26 ± 0.36, that for CA was 35.03 ± 0.63 and that for AAP was 36.07 ± 0.45. The differences between the mean intraoral thermal temperatures of the three diagnostic groups were statistically significant (P < 0.001). The result of the Kappa coefficient of agreement between the combination of clinical and radiographic assessments and the thermographic evaluation was significant (P < 0.001).

Conclusions

Thermography is an effective, quantitative and nonionizing approach that can be used for the diagnosis of periapical inflammatory lesions. The results of the present study indicated that the highest thermal image temperatures were recorded for AA. Thermography might be able to detect inflammatory reactions during the preclinical stage, leading to early diagnosis.

The difference in heat transport characteristics of the heart and lung meridians: A comparative study of COPD patients and healthy subjects

BACKGROUND

The vast majority of previous studies focused on the relationship between 1 meridian and 1 organ, and the comparison and specificity between 2 meridians is rarely explored. Thus, the aim of this study is to compare the heat transport characteristics between 2 different meridians and the specificity between them will also be investigated.

METHODS

The Lung and Heart meridians are chosen for comparison of 2 different meridians. We will enroll 120 subjects and divide them into the healthy control group, chronic obstructive pulmonary disease (COPD) group and healthy intervention group, in a 1:1:1 ratio. Infrared thermography (IRT) will be used to assess the heat transport characteristics of the Heart and Lung meridians. The specificity for the meridian-visceral association will be investigated by comparing the difference in heat transport characteristic between the Heart and Lung meridians in the healthy control group and COPD group. Meanwhile, moxibustion will be given to subjects in the Heart meridian and Lung meridian respectively in the healthy intervention group to verify the specificity for the surface-surface association.

RESULTS

The primary outcomes will be the temperature of corresponding sites along the Heart and Lung meridians.

CONCLUSION

This study will verify the specificity between different meridians by comparing the difference in heat transport characteristic. The findings will guide the selection of acupoints to optimize the therapeutic effect of acupuncture and help determine whether IRT could be used to assist in the diagnosis of COPD.

ETHICS AND DISSEMINATION

The study has been approved by the Third Affiliated Hospital of Zhejiang Chinese Medical University (Approval No. ZSLL-KY-2019-001G-01).

TRIAL REGISTRATION NUMBERS

NCT04046588.

Effect of a Marathon on Skin Temperature Response After a Cold-Stress Test and Its Relationship With Perceptive, Performance, and Oxidative-Stress Biomarkers

CONTEXT

Although skin-temperature assessment has received much attention in recent years as a possible internal-load measurement, scientific evidence is scarce.

PURPOSE

To analyze baseline skin temperature and its rewarming through means of a cold-stress test before and after performing a marathon and to study the association between skin temperature and internal/external-load measurements.

METHODS

A total of 16 runners were measured 48 and 24 h before and 24 and 48 h after completing a marathon. The measurements on each day of testing included urine biomarkers of oxidative stress, pain and fatigue perception, skin temperature (at baseline and after a cold-stress test), and jump performance.

RESULTS

Reduced jump performance (P < .01 and effect size [ES] = 0.5) and higher fatigue and pain perception were observed 24 h after the marathon (P < .01 and ES > 0.8). Although no differences in baseline skin temperature were observed between the 4 measuring days, posterior legs presented lower constant (P < .01 and ES = 1.4) and higher slope (P = .04 and ES = 1.1) parameters in the algorithmic equations fitted for skin-temperature recovery after the cold-stress test 24 h after the marathon than on the day before the marathon. Regressions showed that skin-temperature parameters could be predicted by the ratio of ortho-tyrosine isomer to phenylalanine (oxidative stress biomarker) and body fat composition, among others.

CONCLUSIONS

Although baseline skin temperature was not altered 24 or 48 h after a marathon, the application of cold stress after the marathon would appear to be a good method for providing information on vasoconstriction and a runner's state of stress.

Competitive neural layer-based method to identify people with high risk for diabetic foot

BACKGROUND AND OBJECTIVE

To automatically identify patients with diabetes mellitus (DM) who have high risk of developing diabetic foot, via an unsupervised machine learning technique.

METHODS

We collected a new database containing 54 known risk factors from 250 patients diagnosed with diabetes mellitus. The database also contained a separate validation cohort composed of 73 subjects, where the perceived risk was annotated by expert nurses. A competitive neuron layer-based method was used to automatically split training data into two risk groups.

RESULTS

We found that one of the groups was composed of patients with higher risk of developing diabetic foot. The dominant variables that described group membership via our method agreed with the findings from other studies, and indicated a greater risk for developing such a condition. Our method was validated on the available test data, reaching 71% sensitivity, 100% specificity, and 90% accuracy.

CONCLUSIONS

Unsupervised learning may be deployed to screen patients with diabetes mellitus, pointing out high-risk individuals who require priority follow-up in the prevention of diabetic foot with very high accuracy. The proposed method is automatic and does not require clinical examinations to perform risk assessment, being solely based on the information of a questionnaire answered by patients. Our study found that discriminant variables for predicting risk group membership are highly correlated with expert opinion.

Use of infrared thermography to detect early alterations of peripheral perfusion: evaluation in a porcine model

This study aimed to evaluate the variations of infrared thermography according to rapid hemodynamic changes, by measuring the peripheral skin temperature in a porcine model. Eight healthy piglets were anesthetized and exposed to different levels of arterial pressure. Thermography was performed on the left forelimb to measure carpus and elbow skin temperature and their associated gradient with the core temperature. Changes in skin temperature in response to variations of blood pressure were observed. A negative correlation between arterial pressure and temperature gradients between peripheral and core temperature and a negative correlation between cardiac index and these temperature gradients were observed. Thermography may serve as a tool to detect early changes in peripheral perfusion.

Deep Learning Classification for Diabetic Foot Thermograms

According to the World Health Organization (WHO), Diabetes Mellitus (DM) is one of the most prevalent diseases in the world. It is also associated with a high mortality index. Diabetic foot is one of its main complications, and it comprises the development of plantar ulcers that could result in an amputation. Several works report that thermography is useful to detect changes in the plantar temperature, which could give rise to a higher risk of ulceration. However, the plantar temperature distribution does not follow a particular pattern in diabetic patients, thereby making it difficult to measure the changes. Thus, there is an interest in improving the success of the analysis and classification methods that help to detect abnormal changes in the plantar temperature. All this leads to the use of computer-aided systems, such as those involved in artificial intelligence (AI), which operate with highly complex data structures. This paper compares machine learning-based techniques with Deep Learning (DL) structures. We tested common structures in the mode of transfer learning, including AlexNet and GoogleNet. Moreover, we designed a new DL-structure, which is trained from scratch and is able to reach higher values in terms of accuracy and other quality measures. The main goal of this work is to analyze the use of AI and DL for the classification of diabetic foot thermograms, highlighting their advantages and limitations. To the best of our knowledge, this is the first proposal of DL networks applied to the classification of diabetic foot thermograms. The experiments are conducted over thermograms of DM and control groups. After that, a multi-level classification is performed based on a previously reported thermal change index. The high accuracy obtained shows the usefulness of AI and DL as auxiliary tools to aid during the medical diagnosis.

Visualizing the Knowledge Structure and Research Evolution of Infrared Detection Technology Studies

This paper aims to explore the current status, research trends and hotspots related to the field of infrared detection technology through bibliometric analysis and visualization techniques based on the Science Citation Index Expanded (SCIE) and Social Sciences Citation Index (SSCI) articles published between 1990 and 2018 using the VOSviewer and Citespace software tools. Based on our analysis, we first present the spatiotemporal distribution of the literature related to infrared detection technology, including annual publications, origin country/region, main research organization, and source publications. Then, we report the main subject categories involved in infrared detection technology. Furthermore, we adopt literature cocitation, author cocitation, keyword co-occurrence and timeline visualization analyses to visually explore the research fronts and trends, and present the evolution of infrared detection technology research. The results show that China, the USA and Italy are the three most active countries in infrared detection technology research and that the Centre National de la Recherche Scientifique has the largest number of publications among related organizations. The most prominent research hotspots in the past five years are vibration thermal imaging, pulse thermal imaging, photonic crystals, skin temperature, remote sensing technology, and detection of delamination defects in concrete. The trend of future research on infrared detection technology is from qualitative to quantitative research development, engineering application research and infrared detection technology combined with other detection techniques. The proposed approach based on the scientific knowledge graph analysis can be used to establish reference information and a research basis for application and development of methods in the domain of infrared detection technology studies.

Lipohypertrophic Skin Changes in Patients With Diabetes: Visualization by Infrared Images

BACKGROUND

Many patients with diabetes on insulin therapy develop lipohypertrophies (LHTs). So far, LHTs are diagnosed by conventional methods (CM; visual inspection, palpation and/or ultrasound). In everyday life, it would be advantageous to have a quick, simple and inexpensive alternative, for example, diagnosing them by obtaining infrared (IR) images.

METHODS

We obtained IR images from 43 subjects (21 patients with type 1 diabetes, conventional subcutaneous insulin therapy and known LHTs, 8 patients with CSII and LHTs, 7 patients without LHTs, and 7 healthy people), all from one specialized diabetes practice. The IR images were taken under standardized conditions with a high-resolution infrared camera (VarioCam® HDx Jenoptic, IR pixel 640 × 480, thermal resolution 0.003K) and compared with LHT diagnoses with CM.

RESULTS

In 14 of the 29 (48%) patients, CM diagnosed LHTs were "cold spots" in the IR images. The temperature difference to "healthy" skin (without LHTs) was up to 6°C. Of the 14 patients, 11 also showed such spots, without findings with CM. Four patients did not show clearly identifiable cold spots as LHT and 2 patients showed no changes in the IR images. The remaining 9 patients did not show clearly identifiable cold spots as LHT, but the diagnosis with CM was also ambiguous.

CONCLUSIONS

The results of this small (pilot) study do not clearly support the value of IR images for the diagnosis of LHTs, but they do not refute this approach. Diagnosis of LHT might be hampered due to the existence of different types of LHTs. Usage of IR images can apparently detect LHTs before they can be diagnosed with CM. Further targeted investigations are required to make statements about the usability of this method.

Robust Methods for Real-Time Diabetic Foot Ulcer Detection and Localization on Mobile Devices

Current practice for diabetic foot ulcers (DFU) screening involves detection and localization by podiatrists. Existing automated solutions either focus on segmentation or classification. In this work, we design deep learning methods for real-time DFU localization. To produce a robust deep learning model, we collected an extensive database of 1775 images of DFU. Two medical experts produced the ground truths of this data set by outlining the region of interest of DFU with an annotator software. Using five-fold cross-validation, overall, faster R-CNN with InceptionV2 model using two-tier transfer learning achieved a mean average precision of 91.8%, the speed of 48 ms for inferencing a single image and with a model size of 57.2 MB. To demonstrate the robustness and practicality of our solution to real-time prediction, we evaluated the performance of the models on a NVIDIA Jetson TX2 and a smartphone app. This work demonstrates the capability of deep learning in real-time localization of DFU, which can be further improved with a more extensive data set.

Rapid extraction of the hottest or coldest regions of medical thermographic images

Early detection of breast tumors, feet pre-ulcers diagnosing in diabetic patients, and identifying the location of pain in patients are essential to physicians. Hot or cold regions in medical thermographic images have potential to be suspicious. Hence extracting the hottest or coldest regions in the body thermographic images is an important task. Lazy snapping is an interactive image cutout algorithm that can be applied to extract the hottest or coldest regions in the body thermographic images quickly with easy detailed adjustment. The most important advantage of this technique is that it can provide the results for physicians in real time readily. In other words, it is a good interactive image segmentation algorithm since it has two basic characteristics: (1) the algorithm produces intuitive segmentation that reflects the user intent with given a certain user input and (2) the algorithm is efficient enough to provide instant visual feedback. Comparing to other methods used by the authors for segmentation of breast thermograms such as K-means, fuzzy c-means, level set, and mean shift algorithms, lazy snapping was more user-friendly and could provide instant visual feedback. In this study, twelve test cases were presented and by applying lazy snapping algorithm, the hottest or coldest regions were extracted from the corresponding body thermographic images. The time taken to see the results varied from 7 to 30 s for these twelve cases. It was concluded that lazy snapping was much faster than other methods applied by the authors such as K-means, fuzzy c-means, level set, and mean shift algorithms for segmentation. Graphical abstract Time taken to implement lazy snapping algorithm to extract suspicious regions in different presented thermograms (in seconds). In this study, ten test cases are presented that by applying lazy snapping algorithm, the hottest or coldest regions were extracted from the corresponding body thermographic images. The time taken to see the results varied from 7 to 30 s for the ten cases. It concludes lazy snapping is much faster than other methods applied by the authors.