Biomedical Applications of Infrared Thermal Imaging: Current State of Machine Learning Classification

Infrared thermal (IRT) imaging is a modality that allows non-invasive and non-ionizing monitoring of skin surface temperature distribution, providing underlining physiological information on peripheral blood flow, autonomic nervous system, vasoconstriction/vasodilatation, inflammation, transpiration or other processes that can contribute to skin temperature. This imaging method has been used in biomedical applications since 1956 and has proved its usefulness for vascular, neurological and musculoskeletal pathological situations. This research aims to identify and appraise the recent biomedical applications which had used intelligent analysis methods such as machine learning processes to classify and perform decision making towards improving the existing medical care, a literature review is presented and their operation in the biomedical applications of infrared thermal imaging.


Introduction
The method of infrared thermal (IRT) imaging allows to record and map large areas of the human body skin surface, it is related with the underlying physiology, namely peripheral blood flow and autonomic nervous system. It can be used as a pathological parameter to adjunct clinical decisions such as diagnosis or treatment evaluations, being easy to use, safe and fast. Since mid 50's it has been employed in clinical practice and research with several applications in the vascular, neurological and musculoskeletal systems [1]. International accepted guidelines [2][3][4] were developed to standardize the technique and improve its outcomes and massive fever screening standards were produced [5][6][7][8].
Over this decade loads of data have been generated, which has been per application individually analyzed and statistical evaluated to produce results, but with technology it is possible to generate information from data, knowledge from information, wisdom from knowledge and make decisions based in this generated wisdom with Artificial Intelligence methods such as machine learning (ML). Examples of this methods that have been employed in other areas and medical imaging modalities are: Artificial Neural Networks (ANN), Support Vector Machine (SVM), Naïve Bayes (NB), k-Nearest Neighbour (k-NN), Fuzzy methods, Decision Trees (DT), Random Forest (RF) and AdaBoost.
It is aim of this research to survey the literature sources such as PubMed, Scopus and Web of Knowledge and identify biomedical applications of IRT imaging with usage of data classification with ML methods.

Results of the Literature Survey
The results of the literature survey are presented at table 1, constructed with the year of publication, type of application, ML classifier with better performance, the sample size, the accuracy, sensitivity and specificity.
Sensitivity is related to the test's ability to identify a condition correctly. It is obtained as the number of true positives (TP) divided by the total number of true positives and false negatives (FN) in a population (Equation (1)). Specificity is related to the test's ability to exclude a condition correctly. It is obtained as the number of true negatives (TN) divided by the total number of true negatives and false positives (FP) in a population (Equation (2)). Finally, accuracy is calculated by dividing the total number of successful results by the total population (Equation (3)).

Discussion and Conclusions
Based on the survey, the biomedical applications of IRT imaging using ML classification were: breast cancer detection (21), Diabetic foot disease (6), Skin cancer (3), Carpal Tunnel Syndrome (2), Dry eye disease (2), Back pain, Finger skin injury, Facial nerve function, Thyroid abnormalities, Drunkenness state, Exercise-induced fatigue, Breath analysis, Rheumatoid arthritis, Hypertension, Expression recognition, Burn wounds, Diabetes, Cardiovascular disease, Hemodynamic Shock and Stress recognition.
A comparison of the ML classifiers performance in the biomedical applications is outside of the scope of this survey, since the datasets are different, and it will be addressed in a further publication.
The used ML classifiers in biomedical applications of IRT imaging were ANN (15) The highest accuracy reported, 100%, was using ANN in Breath analysis [38] and k-NN in Finger skin injury [28], the overall assessment parameters better classification was obtained using k-NN in Dry eye disease [23].
Despite the major biomedical application of IRT imaging data with ML classifier being in Breast cancer detection, this application has been not recommended as primary screen method [58].
There is no doubt about the utility and usefulness of data classifiers in biomedical application of IRT imaging, which is still unexplored in many proved applications. This is due to certain barriers, such as the lack of familiarity of the principles and the imaging technique by the health professionals, and the lack of a standard imaging file format, which makes data exchange and integration into information systems and development of advanced Computer Aided Diagnosis tools difficult.
Examples of applications that could have great success in using intelligent data classifiers relate to Raynaud's phenomenon, soft tissues rheumatism, blood pressure, hand-arm vibration syndrome, peripheral nerves compressions, complex regional pain syndrome, fever screening, dermatological disorders, temporomandibular joint conditions, renal dialysis, chemotherapy assessment and rehabilitation medicine procedures assessment. Larger data samples are also required for better overall results.

Conflicts of Interest:
The authors declare no conflicts of interest.