[EMBARGOED UNTIL 6/1/2023] The association between illness and body temperature dates back to the beginnings of medicine. Over the last few years, infrared thermography has attracted increased attention in various medical applications due to technological advancements that reduce manufacturing costs and enable high-resolution cameras with great sensitivity. The recent 2019 coronavirus disease (COVID-19) pandemic revealed that disease transmission control is crucial in preventing an outbreak. Thermography has been considered the gold standard for screening fevered individuals during pandemics since the 2003 SARS outbreak. With the increasing demands for objective and quantitative diagnostic tools in primary health care, thermography offers advantages over conventional approaches as a low-cost, non-invasive, radiation-free, and pain-free method for detecting changes in skin temperature with high spatial accuracy. Several topics are investigated in this dissertation to address the use of infrared imaging as a quantitative tool in medical applications. Peripheral arterial disease (PAD) is a common circulatory problem that affects 202 million patients globally and more than 20 percent of people over 70. The ankle-brachial index (ABI) measurement has been considered the most reliable diagnostic test for PAD. However, some limitations exist where ABI may give false normal values above 1.4 for patients with diabetes or chronic kidney disease with rigid and non-compressible arterial vessel walls. Additionally, ABI must be performed in a vascular laboratory by trained vascular staff to ensure the accuracy of the measurements. During the physical examination, one of the PAD signs is the reduction in the patient's feet and legs' skin temperature, which offers the potential of using infrared thermography in diagnosing PAD. Thus, we conducted a study based on 12 patients recruited by vascular surgeons at the University of Missouri Hospital outpatient clinic. These patients were diagnosed with PAD and scheduled for intervention after clinical evaluation. We developed a framework based on Gaussian Process Regression (GPR) with LOSO cross-validation to correlate the thermal features with the corresponding ABI. Additionally, we proposed a classification model based on the weighted K-Nearest neighbors (WKNN) algorithm to assess the severity of PAD based on infrared thermography. The regression results demonstrate a strong association between the thermal features and the ABI values. The proposed WKNN classification model shows thermography's capabilities in assessing the severity of PAD with high accuracy, sensitivity, and specificity. Since PAD patients are more likely to develop wounds and ulcers in the foot region than in the leg, we conducted another study with 10 PAD patients where the foot and ankle region was selected as the targeted ROI. Using SVM regression with LOSO cross-validation and Bayesian optimization, the proposed also shows a strong correlation between the ABI and the extracted thermal features. These studies demonstrate the potential for thermography to be used as a non-invasive supplementary screening tool for PAD. Endovascular treatments, such as peripheral angioplasty and conventional bypass surgery, have been extensively adopted to treat PAD. Despite promising initial results with surgical interventions, long-term durability leads to more surgeries and an increased risk of amputation, disability, and mortality. Following surgical treatments, the current standard of care is a clinic-based surveillance program consisting of office visits every 3 to 6 months during the patient's lifetime. Surgical intervention failures occur in up to 49 percent of patients after two years. However, any failure that is recognized early can be treated effectively with elective surgery, which significantly minimizes the risk of amputation. Therefore, we proposed a novel home-based, patient-operated early warning system to detect declining perfusion following surgical interventions. The study group consists of 10 PAD patients recruited by vascular surgeons at the University of Missouri Hospital outpatient clinic. We proposed a novel PAD Temperature Index Score (PTI) based on the FCM algorithm to interpret changes in lower limb temperature distribution patterns. The proposed PTI index shows stable and repeatable results in recognizing the impact of the revascularization procedure across different scenarios. Additionally, we examine the potential of deep learning-based thermal image analysis in determining the severity of PAD for our in-home surveillance system. ABI is utilized as ground truth in categorizing PAD severity into three classes (severe, moderate, and mild/normal). Based on 10-fold cross-validation, the model achieved high accuracy, sensitivity, specificity, and precision. As a result, the proposed framework provides frequent surveillance that can result in earlier failure detection, lower the risk of amputation, and improve survival in PAD patients. Detecting abnormal body temperature patterns can help predict sepsis before any other symptoms of infection. As a result, we propose using thermography as a non-invasive tool to measure body temperature patterns and detect abnormalities. We hypothesize that sepsis onset will lead to abnormal body temperature patterns, which can be detected by calculating the temperature difference (ETD) between the core and the body extremities. We use "cold stress" induced by cold water immersion (CWI) as a proxy for sepsis. Toward this goal, we proposed a fully automated methodology for calculating core vs. extremity ETD based on the frontal and lateral view of the face. We developed a framework based on FCM clustering for inner and outer ear localization for the frontal view of the face. While for lateral view, we implement an efficient approach for tracking the tip of the nose and the inner corner of the eyes by using a mixture of Viola-Jones, KLT, and superpixel algorithms. The results produce specific core versus body extremities temperature patterns that can be used to simulate body response to sepsis. Additionally, we proposed a deep learning classification procedure for sepsis detection. The study is based on 140 patients being evaluated for sepsis at the University of Missouri Emergency Department. The result demonstrates the viability of infrared imaging with deep learning models for future use in the emergency department setting to detect sepsis. As a result, these studies highlight the potential of using infrared thermography as a suitable screening approach in different medical applications.
