Application of infrared thermography in computer aided diagnosis

Single 5-centimeter-aperture metalens enabled intelligent lightweight mid-infrared thermographic camera

Existing mid-infrared thermographic cameras rely on a stack of refractive lenses, resulting in bulky and heavy imaging systems that restrict their broader utility. Here, we demonstrate a lightweight metalens-based thermographic camera (MTC) enabled by a single 0.5-mm-thick, 3.7-g-weight, flat, and mass-producible metalens. The large aperture size (5 cm) of our metalens, when combined with an uncooled focal plane array, enables thermal imaging at distances of tens of meters. By computationally removing the veiling glare, our MTC realizes the temperature mapping with an inaccuracy of less than ±0.7% within the range of 35° to 700°C and shows exceptional environmental adaptability. Furthermore, by using intelligent algorithms and spectral filtering, our uncooled MTC enables visualization and quantification of the SF6 gas leakage at a long distance of 5 m, with a remarkable minimum detectable leak rate of 0.2 sccm. Our work opens the door to the lightweight and multifunctional intelligent thermal imaging systems.

Thermal mapping the eye: A critical review of advances in infrared imaging for disease detection

Infrared thermography (IRT) has become more accessible due to technological advancements, making thermal cameras more affordable. Infrared thermal cameras capture the infrared rays emitted by objects and convert it into temperature representations. IRT has emerged as a promising and non-invasive approach for examining the human eye. Ocular surface temperature assessment based on IRT is vital for the diagnosis and monitoring of various eye conditions like dry eye, diabetic retinopathy, glaucoma, allergic conjunctivitis, and inflammatory diseases. A collective sum of 192 articles was sourced from various databases, and through adherence to the PRISMA guidelines, 29 articles were ultimately chosen for systematic analysis. This systematic review article seeks to provide readers with a thorough understanding of IRT's applications, advantages, limitations, and recent developments in the context of eye examinations. It covers various aspects of IRT-based eye analysis, including image acquisition, processing techniques, ocular surface temperature measurement, three different approaches to identifying abnormalities, and different evaluation metrics used. Our review also delves into recent advancements, particularly the integration of machine learning and deep learning algorithms into IRT-based eye examinations. Our systematic review not only sheds light on the current state of research but also outlines promising future prospects for the integration of infrared thermography in advancing eye health diagnostics and care.

Thermal imaging and computer vision technologies for the enhancement of pig husbandry: a review

Pig farming, a vital industry, necessitates proactive measures for early disease detection and crush symptom monitoring to ensure optimum pig health and safety. This review explores advanced thermal sensing technologies and computer vision-based thermal imaging techniques employed for pig disease and piglet crush symptom monitoring on pig farms. Infrared thermography (IRT) is a non-invasive and efficient technology for measuring pig body temperature, providing advantages such as non-destructive, long-distance, and high-sensitivity measurements. Unlike traditional methods, IRT offers a quick and labor-saving approach to acquiring physiological data impacted by environmental temperature, crucial for understanding pig body physiology and metabolism. IRT aids in early disease detection, respiratory health monitoring, and evaluating vaccination effectiveness. Challenges include body surface emissivity variations affecting measurement accuracy. Thermal imaging and deep learning algorithms are used for pig behavior recognition, with the dorsal plane effective for stress detection. Remote health monitoring through thermal imaging, deep learning, and wearable devices facilitates non-invasive assessment of pig health, minimizing medication use. Integration of advanced sensors, thermal imaging, and deep learning shows potential for disease detection and improvement in pig farming, but challenges and ethical considerations must be addressed for successful implementation. This review summarizes the state-of-the-art technologies used in the pig farming industry, including computer vision algorithms such as object detection, image segmentation, and deep learning techniques. It also discusses the benefits and limitations of IRT technology, providing an overview of the current research field. This study provides valuable insights for researchers and farmers regarding IRT application in pig production, highlighting notable approaches and the latest research findings in this field.

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.

Infrared Thermography for Real-Time Assessment of the Effectiveness of Scoliosis Braces

This work proposes an innovative method, based on the use of low-cost infrared thermography (IRT) instrumentation, to assess in real time the effectiveness of scoliosis braces. Establishing the effectiveness of scoliosis braces means deciding whether the pressure exerted by the brace on the patient's back is adequate for the intended therapeutic purpose. Traditionally, the evaluation of brace effectiveness relies on empirical, qualitative assessments carried out by orthopedists during routine follow-up examinations. Hence, it heavily depends on the expertise of the orthopedists involved. In the state of the art, the only objective methods used to confirm orthopedists' opinions are based on the evaluation of how scoliosis progresses over time, often exposing people to ionizing radiation. To address these limitations, the method proposed in this work aims to provide a real-time, objective assessment of the effectiveness of scoliosis braces in a non-harmful way. This is achieved by exploiting the thermoelastic effect and correlating temperature changes on the patient's back with the mechanical pressure exerted by the braces. A system based on this method is implemented and then validated through an experimental study on 21 patients conducted at an accredited orthopedic center. The experimental results demonstrate a classification accuracy slightly below 70% in discriminating between adequate and inadequate pressure, which is an encouraging result for further advancement in view of the clinical use of such systems in orthopedic centers.

Automatic Diabetic Foot Ulcer Recognition Using Multi-Level Thermographic Image Data

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.

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.

Empirical Review of Various Thermography-Based Computer-Aided Diagnostic Systems for Multiple Diseases

The lifestyle led by today’s generation and its negligence towards health is highly susceptible to various diseases. Developing countries are at a higher risk of mortality due to late-stage presentation, inaccessible diagnosis, and high-cost treatment. Thermography-based technology, aided with machine learning, for screening inflammation in the human body is non-invasive and cost-wise appropriate. It requires very little equipment, especially in rural areas with limited facilities. Recently, Thermography based monitoring has been deployed worldwide at various organizations and public gathering points as a first measure of screening COVID-19 patients. In this paper, we systematically compare the state-of-the-art feature extraction approaches for analyzing thermal patterns in the human body, individually and in combination, on a platform, using three publicly available Datasets of medical thermal imaging, four Feature Selection methods, and four well-known Classifiers, and analyze the results. We developed and used a 2-level sampling method for training and testing the classification model. Among all the combinations considered, the classification model with Unified Feature-Sets gave the best performance for all the datasets. Also, the experimental results show that the classification accuracy improves considerably with the use of feature selection methods. We obtained the best performance with a features subset of 45, 57, and 39 features (from Unified Feature Set) with a combination of mRMR and SVM for DB-DMR-IR and DB-FOOT-IR and a combination of ReF and RF for DB-THY-IR. Also, we found that for all the feature subsets, the features obtained are relevant, non-redundant, and distinguish normal and abnormal thermal patterns with the accuracy of 94.75% on the DB-DMR-IR dataset, 93.14% on the DB-FOOT-IR dataset, and 92.06% on the DB-THY-IR dataset.

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.

Investigating the Therapeutic Effect of Intradermal Acupuncture for Acute Herpes Zoster and Assessing the Feasibility of Infrared Thermography for Early Prediction of Postherpetic Neuralgia: Study Protocol for a Randomized, Sham-Controlled, Clinical Trial

Introduction

Herpes zoster (HZ) can adversely influence patients' quality of life and sometimes it can develop postherpetic neuralgia (PHN). To date, it remains challenging to be managed by currently available therapies. Intradermal acupuncture (IDA) has the potential to be an adjunctive therapy for acute HZ and infrared thermography (IRT) may be useful for predicting PHN; however, current evidence remains inconclusive. Therefore, the purposes of this trial are to 1) evaluate the efficacy and safety of IDA as an adjunctive therapy for acute HZ; 2) to explore the feasibility of IRT for early prediction of PHN and as an objective tool to aid in subjective pain assessment in acute HZ.

Methods

This study is designed as a randomized, parallel-group, sham-controlled, and patient-assessor-blinded trial, including 1-month treatment and 3-month follow-ups. Seventy-two qualified participants will be randomly split into the IDA or sham IDA group in a ratio of 1:1. Apart from standard pharmacological treatments in both groups, the two groups will receive 10 sessions of IDA or sham IDA, respectively. Primary outcome measures are the visual analog scale (VAS), indicators of herpes lesions' recovery, the temperature of the pain area, and the incidence rate of PHN. The secondary outcome is the 36-item Short Form Health Survey (SF-36). Indicators of herpes lesions' recovery will be assessed at each visit and follow-ups. The remaining outcomes will be assessed at baseline, 1 month after intervention, and 3-month follow-up. Safety evaluation will be determined by adverse events during the trial.

Conclusion

Expected results will determine whether IDA can enhance therapeutic effectiveness of pharmacotherapy for acute HZ with acceptable safety profile. In addition, it will verify the accuracy of IRT for early prediction of PHN and as an objective tool of subjective pain for acute HZ.

Trial Registration

Clinicaltrials.gov (identification number: NCT05348382; Registered 27 April 2022, https://clinicaltrials.gov/ct2/show/NCT05348382).

Theory and applications of InfraRed and thermal image analysis in ergonomics research

Designing products and services to fit human needs, wants and lifestyle require meaningful data. With Industry 4.0 and the internet of things, we have many ways to capture data using sensors and other means. InfraRed (IR) cameras are quite ubiquitous, especially for screening illness and wellness. They can provide a wealth of data on different objects and even people. However, their use has been limited due to processing complexities. With reducing cost and increasing accuracy of IR cameras, access to thermal data is becoming quite widespread, especially in medicine and people-related applications. These cameras have software to help process the data, with a focus on qualitative analyses and rather primitive quantitative analyses. In ergonomics, data from multiple users are essential to make reasonable predictions for a given population. In this study, using 4 simple experiments, several quantitative analysis techniques such as simple statistics, multivariate statistics, geometric modeling, and Fourier series modeling are applied to IR images and videos to extract essential user and population data. Results show that IR data can be useful to provide user and population data that are important for design. More research in modeling IR data and application software is needed for the increased application of IR information in ergonomics applications.

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.

COVID-19 classification using thermal images

SIGNIFICANCE

There is a scarcity of published research on the potential role of thermal imaging in the remote detection of respiratory issues due to coronavirus disease-19 (COVID-19). This is a comprehensive study that explores the potential of this imaging technology resulting from its convenient aspects that make it highly accessible: it is contactless, noninvasive, and devoid of harmful radiation effects, and it does not require a complicated installation process.

AIM

We aim to investigate the role of thermal imaging, specifically thermal video, for the identification of SARS-CoV-2-infected people using infrared technology and to explore the role of breathing patterns in different parts of the thorax for the identification of possible COVID-19 infection.

APPROACH

We used signal moment, signal texture, and shape moment features extracted from five different body regions of interest (whole upper body, chest, face, back, and side) of images obtained from thermal video clips in which optical flow and super-resolution were used. These features were classified into positive and negative COVID-19 using machine learning strategies.

RESULTS

COVID-19 detection for male models [receiver operating characteristic (ROC) area under the ROC curve (AUC) = 0.605 95% confidence intervals (CI) 0.58 to 0.64] is more reliable than for female models (ROC AUC = 0.577 95% CI 0.55 to 0.61). Overall, thermal imaging is not very sensitive nor specific in detecting COVID-19; the metrics were below 60% except for the chest view from males.

CONCLUSIONS

We conclude that, although it may be possible to remotely identify some individuals affected by COVID-19, at this time, the diagnostic performance of current methods for body thermal imaging is not good enough to be used as a mass screening tool.

Skin Cancer Detection Using Infrared Thermography: Measurement Setup, Procedure and Equipment

Infrared thermography technology has improved dramatically in recent years and is gaining renewed interest in the medical community for applications in skin tissue identification applications. However, there is still a need for an optimized measurement setup and protocol to obtain the most appropriate images for decision making and further processing. Nowadays, various cooling methods, measurement setups and cameras are used, but a general optimized cooling and measurement protocol has not been defined yet. In this literature review, an overview of different measurement setups, thermal excitation techniques and infrared camera equipment is given. It is possible to improve thermal images of skin lesions by choosing an appropriate cooling method, infrared camera and optimized measurement setup.

The Current State of Optical Sensors in Medical Wearables

Optical sensors play an increasingly important role in the development of medical diagnostic devices. They can be very widely used to measure the physiology of the human body. Optical methods include PPG, radiation, biochemical, and optical fiber sensors. Optical sensors offer excellent metrological properties, immunity to electromagnetic interference, electrical safety, simple miniaturization, the ability to capture volumes of nanometers, and non-invasive examination. In addition, they are cheap and resistant to water and corrosion. The use of optical sensors can bring better methods of continuous diagnostics in the comfort of the home and the development of telemedicine in the 21st century. This article offers a large overview of optical wearable methods and their modern use with an insight into the future years of technology in this field.

Artificial intelligence and infrared thermography as auxiliary tools in the diagnosis of temporomandibular disorder

OBJECTIVE

To assess three machine learning (ML) attribute extraction methods: radiomic, semantic and radiomic-semantic association on temporomandibular disorder (TMD) detection using infrared thermography (IT); and to determine which ML classifier, KNN, SVM and MLP, is the most efficient for this purpose.

METHODS AND MATERIALS

78 patients were selected by applying the Fonseca questionnaire and RDC/TMD to categorize control patients (37) and TMD patients (41). IT lateral projections of each patient were acquired. The masseter and temporal muscles were selected as regions of interest (ROI) for attribute extraction. Three methods of extracting attributes were assessed: radiomic, semantic and radiomic-semantic association. For radiomic attribute extraction, 20 texture attributes were assessed using co-occurrence matrix in a standardized angulation of 0°. The semantic features were the ROI mean temperature and pain intensity data. For radiomic-semantic association, a single dataset composed of 28 features was assessed. The classification algorithms assessed were KNN, SVM and MLP. Hopkins's statistic, Shapiro-Wilk, ANOVA and Tukey tests were used to assess data. The significance level was set at 5% (p < 0.05).

RESULTS

Training and testing accuracy values differed statistically for the radiomic-semantic association (p = 0.003). MLP differed from the other classifiers for the radiomic-semantic association (p = 0.004). Accuracy, precision and sensitivity values of semantic and radiomic-semantic association differed statistically from radiomic features (p = 0.008, p = 0.016 and p = 0.013).

CONCLUSION

Semantic and radiomic-semantic-associated ML feature extraction methods and MLP classifier should be chosen for TMD detection using IT images and pain scale data. IT associated with ML presents promising results for TMD detection.

Advances in Thermal Image Analysis for the Detection of Pregnancy in Horses Using Infrared Thermography

Infrared thermography (IRT) was applied as a potentially useful tool in the detection of pregnancy in equids, especially native or wildlife. IRT measures heat emission from the body surface, which increases with the progression of pregnancy as blood flow and metabolic activity in the uterine and fetal tissues increase. Conventional IRT imaging is promising; however, with specific limitations considered, this study aimed to develop novel digital processing methods for thermal images of pregnant mares to detect pregnancy earlier with higher accuracy. In the current study, 40 mares were divided into non-pregnant and pregnant groups and imaged using IRT. Thermal images were transformed into four color models (RGB, YUV, YIQ, HSB) and 10 color components were separated. From each color component, features of image texture were obtained using Histogram Statistics and Grey-Level Run-Length Matrix algorithms. The most informative color/feature combinations were selected for further investigation, and the accuracy of pregnancy detection was calculated. The image texture features in the RGB and YIQ color models reflecting increased heterogeneity of image texture seem to be applicable as potential indicators of pregnancy. Their application in IRT-based pregnancy detection in mares allows for earlier recognition of pregnant mares with higher accuracy than the conventional IRT imaging technique.

Facial Paralysis Detection in Infrared Thermal Images Using Asymmetry Analysis of Temperature and Texture Features

Facial temperature distribution in healthy people shows contralateral symmetry, which is generally disrupted by facial paralysis. This study aims to develop a quantitative thermal asymmetry analysis method for early diagnosis of facial paralysis in infrared thermal images. First, to improve the reliability of thermal image analysis, the facial regions of interest (ROIs) were segmented using corner and edge detection. A new temperature feature was then defined using the maximum and minimum temperature, and it was combined with the texture feature to represent temperature distribution of facial ROIs. Finally, Minkowski distance was used to measure feature symmetry of bilateral ROIs. The feature symmetry vectors were input into support vector machine to evaluate the degree of facial thermal symmetry. The results showed that there were significant differences in thermal symmetry between patients with facial paralysis and healthy people. The accuracy of the proposed method for early diagnosis of facial paralysis was 0.933, and the area under the ROC curve was 0.947. In conclusion, temperature and texture features can effectively quantify thermal asymmetry caused by facial paralysis, and the application of machine learning in early detection of facial paralysis in thermal images is feasible.

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.

Infrared Thermal Imaging of Patients With Acute Upper Respiratory Tract Infection: Mixed Methods Analysis

Background

Upper respiratory tract infection is a common disease of the respiratory system. Its incidence is very high, and it can even cause pandemics. Infrared thermal imaging (IRTI) can provide an objective and quantifiable reference for the visual diagnosis of people with acute respiratory tract infection, and it can function as an effective indicator of clinical diagnosis.

Objective

The aims of this study are to observe and analyze the infrared expression location and characteristics of patients with acute upper respiratory tract infection through IRTI technology and to clearly express the quantification of temperature, analyze the role of IRTI in acute upper respiratory tract diagnostic research, and understand the impact of IRTI in qualitative and quantitative research.

Methods

From December 2018 to February 2019, 154 patients with acute upper respiratory tract infection were randomly selected from the emergency department of the First Affiliated Hospital of Guangzhou Medical University. Among these patients, 73 were men and 81 were women. The subjects were divided into two groups according to the presence of fever, namely, fever and nonfever groups. Qualitative and quantitative analyses of the infrared thermal images were performed to compare the results before and after application of the technology.

Results

Using the method described in this paper, through the analysis of experimental data, we elucidated the role of IRTI in the diagnosis of acute upper respiratory tract infection, and we found that qualitative and quantitative IRTI analyses play important roles. Through the combination of theory and experimental data, the IRTI analysis showed good results in identifying acute upper respiratory tract infection.

Conclusions

IRTI technology plays an important role in identifying the infrared expression location and characteristics of patients with acute upper respiratory tract infection as well as in the quantification of clear expression of body temperature, and it provides an objective and quantifiable reference basis for elucidating the pathogenesis of these patients.

[Connected bras for breast cancer detection in 2021: Analysis and perspectives]

OBJECTIVES

Breast cancer is the leading cancer in women worldwide with about 2 million new cases and 685,000 deaths each year. Mammography is the most widely used screening and diagnostic method. Currently, digital technologies advances facilitate the development of connected and portable devices. To overcome some of the disadvantages of mammography (breast compression, difficulty in analyzing dense breasts, radiation, limited accessibility in some countries, etc.), portable devices, conventionally known as connected bras (CB), have been created to offer an alternative method to mammography. The objective of our review was to list all the published CBs in order to know their main characteristics, their potential indications and their possible limitations.

METHOD

A bibliographical search in the PUBMED database selecting only articles written in French or English, between 2011 and 2020, found 7 CBs under development.

RESULTS

These CBs use thermal, ultrasonic and impedance sensors. Their advantages are an absence of irradiation, an absence of breast compression and a flexibility of use (outside an X-ray cabinet). Mammary gland analysis times vary, depending on the device, between 30min and 24h. They are all connected to data transmission systems and models that analyze the results.

DISCUSSION AND CONCLUSION

These CBs are mostly still undergoing clinical validation (only [iTBra] has been evaluated in a clinical trial) and require evaluation steps that will eventually allow their future use for breast cancer detection in high-risk women, particularly in women with dense breasts and in women between screening waves.

Review of the efficacy of infrared thermography for screening infectious diseases with applications to COVID-19

Purpose: The recent coronavirus disease 2019 (COVID-19) pandemic, which spread across the globe in a very short period of time, revealed that the transmission control of disease is a crucial step to prevent an outbreak and effective screening for viral infectious diseases is necessary. Since the severe acute respiratory syndrome (SARS) outbreak in 2003, infrared thermography (IRT) has been considered a gold standard method for screening febrile individuals at the time of pandemics. The objective of this review is to evaluate the efficacy of IRT for screening infectious diseases with specific applications to COVID-19. Approach: A literature review was performed in Google Scholar, PubMed, and ScienceDirect to search for studies evaluating IRT screening from 2002 to present using relevant keywords. Additional literature searches were done to evaluate IRT in comparison to traditional core body temperature measurements and assess the benefits of measuring additional vital signs for infectious disease screening. Results: Studies have reported on the unreliability of IRT due to poor sensitivity and specificity in detecting true core body temperature and its inability to identify asymptomatic carriers. Airport mass screening using IRT was conducted during occurrences of SARS, Dengue, Swine Flu, and Ebola with reported sensitivities as low as zero. Other studies reported that screening other vital signs such as heart and respiratory rates can lead to more robust methods for early infection detection. Conclusions: Studies evaluating IRT showed varied results in its efficacy for screening infectious diseases. This suggests the need to assess additional physiological parameters to increase the sensitivity and specificity of non-invasive biosensors.

A computer aided diagnostic method for the evaluation of type II diabetes mellitus in facial thermograms

Almost 50% of individuals around the globe are unaware of diabetes and its complications. So, an early screening of diabetes is very important at this current situation. To overcome the difficulties such as pain and discomfort to the subjects obtained from the biochemical diagnostic procedures; an infrared thermography is the diagnostic technique which measures the skin surface temperature noninvasively. Thus, the aim of our proposed study was to evaluate the type II diabetes in facial thermograms and to develop a computer aided diagnosis (CAD) system to classify the normal and diabetes. The facial thermograms (n = 160) including male (n = 79) and female (n = 81) were captured using FLIR A 305sc infrared thermal camera. The Haralick textural features were extracted from the facial thermograms based on gray level co-occurrence matrix algorithm. The T_ROI, T_MAX, and T_TOT are the statistical temperature parameters exhibited a significant negative correlation with HbA1c (r = - 0.421, - 0.411, - 0.242, p < 0.01 (T_ROI); r = - 0.259, p < 0.01(T_MAX) and - 0.173, p < 0.05 (T_TOT)). An optimal regression equation has been constructed by using the significant facial variables and standard HbA1c values. The model has achieved sensitivity, specificity, and accuracy rate as 91.42%, 88.57%, and 90% respectively. The anthropometrical variables, extracted textural features and temperature parameters were fed into the classifiers and their performances were compared. The Support Vector Machine outperformed the Linear Discriminant Analysis (84.37%) and k-Nearest Neighbor (81.25%) classifiers with the maximum accuracy rate of 89.37%. The developed CAD system has achieved 89.37% of accuracy rate for the classification of diabetes. Thus, the facial thermography could be used as the basic non-invasive prognostic tool for the evaluation of type II diabetes mellitus.

Occupational Risk Evaluation Through Infrared Thermography: Development and Proposal of a Rapid Screening Tool for Risk Assessment Arising from Repetitive Actions of the Upper Limbs

Risk analysis is one of the main tools for preventing the occurrence of Work-Related Musculoskeletal Disorders. New methods of risk analysis should seek to be more agile and simplified, encouraging them to be widely applied in work environments. This paper aimed to develop a rapid tool for assessing the risk of developing Work-Related Musculoskeletal Disorders (WMSDs) arising from repetitive actions of the upper limbs, while using a thermographic camera to measure skin temperature variation. A workstation was developed in an environmentally controlled laboratory, representing the five levels of risk presented by the Occupational Repetitive Actions Index (OCRA) Index, which were performed by 32 participants for 20 min. each level. There was a significant change in forearm skin temperature at all risk levels (p < 0.001), with a positive linear correlation (r = 0.658 and p < 0.001), which led the authors to perform linear regression analysis for the forearm region. The Predicted OCRA Index calculation equation was successfully developed (R = 0.767 and R² = 0.588), while using as independent variables: air temperature and temperature variation of the forearm skin. The Predicted OCRA Index can be applied as a screening tool for large numbers of workers in the same company or sector, due to its speed of application and the determination of risk level, but it does not replace the original OCRA Index.

Infrared thermographic investigation on the ocular surface temperature of normal subjects

OBJECTIVE

It is understood that the ability to measure ocular temperature accurately will increase understanding of ocular physiology and should be a support in decision-making in classical diagnostic procedures. The use of ocular thermography offers great opportunities for monitoring the temperature of the anterior eye and analyzing the effects of certain pathologies on ocular surface temperature (OST). The aim of the present work is to measure the OST of 220 healthy normal subjects, stratified according to gender and age, in order to obtain a normal temperature distribution to be used as reference values when comparing healthy versus pathological conditions.

APPROACH

The OST is measured from five regions, located over the whole area of the anterior eye, which correspond to particular anatomic structures, through a semi-automated procedure to post-process the infrared images. The relationship between OST and independent variables (forehead skin temperature, age, gender, level of physical activity, cardiovascular risk factors including sedentary lifestyle and smoking, laboratory temperature, and laboratory humidity) was investigated through linear regression models.

MAIN RESULTS

The OSTs measured from the five different ocular regions are statistically different (p -value  <  0.001), even when dividing our subjects into males and females, with the nasal cantus being the hottest region and the central cornea the coolest; when considering also the effect of age, stratifying our subjects into young, middle-aged and elderly, the OST decreases when age increases significantly. Statistical analysis based on linear regression models pointed out that age, forehead skin temperature, and lab temperature are the main factors to be taken into account when exploring the OST.

SIGNIFICANCE

As OST evaluation can be important in detecting different ocular pathologies, having precise details of the variation in temperature across the ocular surface and therefore a more detailed map of the OST adjusted according to subject characteristics and environment conditions could enhance early diagnosis and thus course of treatments.

The microclimate under dressings applied to intact weight-bearing skin: Infrared thermography studies

BACKGROUND

When a patient is lying in a hospital bed (e.g. supine or prone), bodyweight forces distort soft tissues by compression, tension and shear, and may lead to the onset of pressure ulcers in those who are stationary and insensate, especially at their pelvic region. Altered localized microclimate conditions, particularly elevated skin temperatures leading to perspiration and resulting in skin moisture or wetness, are known to further increase the risk for pressure ulcers, which is already high in immobile patients.

METHODS

We have used infrared thermography to measure local skin temperatures at the buttocks of supine healthy subjects, to quantitatively determine, for the first time in the literature, how skin microclimate conditions associated with a weight-bearing Fowler's position are affected by application of dressings. Our present methodology has been applied to compare a polymeric membrane dressing versus placebo foam, with a no-dressing case used as reference.

FINDINGS

One hour of lying in a Fowler's position was already enough to cause considerable heat trapping (~3 °C rise) between the weight-bearing body and the support surface. Analyses of normalized local skin temperatures and entropy of the temperature distributions indicated that the polymeric membrane dressing material allowed better and more homogenous clearance of locally accumulated body-heat with respect to simple foam.

INTERPRETATION

Infrared thermography is suitable for characterizing skin microclimate conditions under different dressings, and, accordingly, is effective in developing and evaluating pressure ulcer prevention and treatment strategies - both of which require adequate skin microclimate.

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.

Breast Cancer Detection in Thermal Infrared Images Using Representation Learning and Texture Analysis Methods

Nowadays, breast cancer is one of the most common cancers diagnosed in women. Mammography is the standard screening imaging technique for the early detection of breast cancer. However, thermal infrared images (thermographies) can be used to reveal lesions in dense breasts. In these images, the temperature of the regions that contain tumors is warmer than the normal tissue. To detect that difference in temperature between normal and cancerous regions, a dynamic thermography procedure uses thermal infrared cameras to generate infrared images at fixed time steps, obtaining a sequence of infrared images. In this paper, we propose a novel method to model the changes on temperatures in normal and abnormal breasts using a representation learning technique called learning-to-rank and texture analysis methods. The proposed method generates a compact representation for the infrared images of each sequence, which is then exploited to differentiate between normal and cancerous cases. Our method produced competitive (AUC = 0.989) results when compared to other studies in the literature.

Infrared Thermography and Soft Computing for Diabetic Foot Assessment

Recent advancements in digital image processing and soft computing techniques have widened the scope of computer aided diagnosis of medical conditions. Many imaging modalities like MRI, CT, PET, Ultrasound combined with soft computing techniques is already contributing to this trend. With the recent inclusion of infrared thermal imaging, the capability of computer aided diagnosis has increased and has become more safe and convenient. Research in this noncontact and noninvasive imaging technology has steadily increased over the last 50 years. Disease diagnosis based on the correlation of surface temperature distribution of skin is being studied at large and has shown promising results. This chapter will give the reader a solid understanding of the theory behind infrared thermography and the use of soft computing techniques applied to medical image analysis, particularly for diabetic foot complication assessment. The issues and challenges to be addressed in using infrared thermography for diagnostic purposes are also discussed. The reader will get a complete overview of building an intelligent diagnostic system using the two sensational topics of research in machine learning and medical imaging—infrared thermography and soft computing.

Deep learning for healthcare applications based on physiological signals: A review

BACKGROUND AND OBJECTIVE

We have cast the net into the ocean of knowledge to retrieve the latest scientific research on deep learning methods for physiological signals. We found 53 research papers on this topic, published from 01.01.2008 to 31.12.2017.

METHODS

An initial bibliometric analysis shows that the reviewed papers focused on Electromyogram(EMG), Electroencephalogram(EEG), Electrocardiogram(ECG), and Electrooculogram(EOG). These four categories were used to structure the subsequent content review.

RESULTS

During the content review, we understood that deep learning performs better for big and varied datasets than classic analysis and machine classification methods. Deep learning algorithms try to develop the model by using all the available input.

CONCLUSIONS

This review paper depicts the application of various deep learning algorithms used till recently, but in future it will be used for more healthcare areas to improve the quality of diagnosis.

Bioinspired Infrared Sensing Materials and Systems

Bioinspired engineering offers a promising alternative approach in accelerating the development of many man-made systems. Next-generation infrared (IR) sensing systems can also benefit from such nature-inspired approach. The inherent compact and uncooled operation of biological IR sensing systems provides ample inspiration for the engineering of portable and high-performance artificial IR sensing systems. This review overviews the current understanding of the biological IR sensing systems, most of which are thermal-based IR sensors that rely on either bolometer-like or photomechanic sensing mechanism. The existing efforts inspired by the biological IR sensing systems and possible future bioinspired approaches in the development of new IR sensing systems are also discussed in the review. Besides these biological IR sensing systems, other biological systems that do not have IR sensing capabilities but can help advance the development of engineered IR sensing systems are also discussed, and the related engineering efforts are overviewed as well. Further efforts in understanding the biological IR sensing systems, the learning from the integration of multifunction in biological systems, and the reduction of barriers to maximize the multidiscipline collaborations are needed to move this research field forward.

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

Diabetes mellitus (DM) is a chronic metabolic disorder that requires regular medical care to prevent severe complications. The elevated blood glucose level affects the eyes, blood vessels, nerves, heart, and kidneys after the onset. The affected blood vessels (usually due to atherosclerosis) may lead to insufficient blood circulation particularly in the lower extremities and nerve damage (neuropathy), which can result in serious foot complications. Hence, an early detection and treatment can prevent foot complications such as ulcerations and amputations. Clinicians often assess the diabetic foot for sensory deficits with clinical tools, and the resulting foot severity is often manually evaluated. The infrared thermography is a fast, nonintrusive and non-contact method which allows the visualization of foot plantar temperature distribution. Several studies have proposed infrared thermography-based computer aided diagnosis (CAD) methods for diabetic foot. Among them, the asymmetric temperature analysis method is more superior, as it is easy to implement, and yielded satisfactory results in most of the studies. In this paper, the diabetic foot, its pathophysiology, conventional assessments methods, infrared thermography and the different infrared thermography-based CAD analysis methods are reviewed.

Supportive Noninvasive Tool for the Diagnosis of Breast Cancer Using a Thermographic Camera as Sensor

Breast cancer is the leading disease in incidence and mortality among women in developing countries. The opportune diagnosis of this disease strengthens the survival index. Mammography application is limited by age and periodicity. Temperature is a physical magnitude that can be measured by using multiple sensing techniques. IR (infrared) thermography using commercial cameras is gaining relevance in industrial and medical applications because it is a non-invasive and non-intrusive technology. Asymmetrical temperature in certain human body zones is associated with cancer. In this paper, an IR thermographic sensor is applied for breast cancer detection. This work includes an automatic breast segmentation methodology, to spot the hottest regions in thermograms using the morphological watershed operator to help the experts locate the tumor. A protocol for thermogram acquisition considering the required time to achieve a thermal stabilization is also proposed. Breast thermograms are evaluated as thermal matrices, instead of gray scale or false color images, increasing the certainty of the provided diagnosis. The proposed tool was validated using the Database for Mastology Research and tested in a voluntary group of 454 women of different ages and cancer stages with good results, leading to the possibility of being used as a supportive tool to detect breast cancer and angiogenesis cases.

COMPUTER AIDED DIAGNOSIS FOR CARDIOVASCULAR DISEASES BASED ON ECG SIGNALS: A SURVEY

The interpretation of Electroencephalography (ECG) signals is difficult, because even subtle changes in the waveform can indicate a serious heart disease. Furthermore, these waveform changes might not be present all the time. As a consequence, it takes years of training for a medical practitioner to become an expert in ECG-based cardiovascular disease diagnosis. That training is a major investment in a specific skill. Even with expert ability, the signal interpretation takes time. In addition, human interpretation of ECG signals causes interoperator and intraoperator variability. ECG-based Computer-Aided Diagnosis (CAD) holds the promise of improving the diagnosis accuracy and reducing the cost. The same ECG signal will result in the same diagnosis support regardless of time and place. This paper introduces both the techniques used to realize the CAD functionality and the methods used to assess the established functionality. This survey aims to instill trust in CAD of cardiovascular diseases using ECG signals by introducing both a conceptional overview of the system and the necessary assessment methods.