Automated thermal imaging monitors the local response to cervical cancer brachytherapy

Artificial intelligence in interventional radiotherapy (brachytherapy): Enhancing patient-centered care and addressing patients' needs

This review explores the integration of artificial intelligence (AI) in interventional radiotherapy (IRT), emphasizing its potential to streamline workflows and enhance patient care. Through a systematic analysis of 78 relevant papers spanning from 2002 to 2024, we identified significant advancements in contouring, treatment planning, outcome prediction, and quality assurance. AI-driven approaches offer promise in reducing procedural times, personalizing treatments, and improving treatment outcomes for oncological patients. However, challenges such as clinical validation and quality assurance protocols persist. Nonetheless, AI presents a transformative opportunity to optimize IRT and meet evolving patient needs.

Novel Diagnostic Approach for Acute Pharyngitis: Combining Machine Learning With Thermal Imaging

We evaluated the effect of infrared thermography (IRT) on the clinical assessment of bacterial and viral pharyngitis and its impact on the predictive value of the McIsaac score algorithm for streptococcal pharyngitis in children. We also investigated if IRT could distinguish between bacterial and viral pharyngitis. The study included children aged 2-17 years presenting with sore throat and fever over 38°C from November 1, 2021, to April 30, 2022. Of the 76 assessed children, 16 were excluded due to missing data or technical issues, leaving 60 children (32 males, 28 females) divided into three groups: Group A with streptococcal pharyngitis (N = 30), viral pharyngitis (N = 16), and healthy controls (N = 14). McIsaac score and IRT imaging showed a 90% positive predictive value for streptococcal pharyngitis. While IRT alone could not distinguish between bacterial and viral infections, it significantly increased the predictive value when combined with the McIsaac score.

Smartphone-based detection of COVID-19 and associated pneumonia using thermal imaging and a transfer learning algorithm

COVID-19-related pneumonia is typically diagnosed using chest x-ray or computed tomography images. However, these techniques can only be used in hospitals. In contrast, thermal cameras are portable, inexpensive devices that can be connected to smartphones. Thus, they can be used to detect and monitor medical conditions outside hospitals. Herein, a smartphone-based application using thermal images of a human back was developed for COVID-19 detection. Image analysis using a deep learning algorithm revealed a sensitivity and specificity of 88.7% and 92.3%, respectively. The findings support the future use of noninvasive thermal imaging in primary screening for COVID-19 and associated pneumonia.

Thermographic Changes following Short-Term High-Intensity Anaerobic Exercise

Current studies report thermographic changes following aerobic or resistance exercise but not short, vigorous anaerobic exercise. Therefore, we investigated body surface temperature changes using thermal imaging following a short session of anaerobic exercise. We studied three different regions of interest (ROIs): the legs, chest, and forehead. Thermal imaging for each participant was performed before and immediately after completing a Wingate anaerobic test and every minute during a 15 min recovery period. Immediately after the test, the maximum temperature was significantly higher in all ROIs (legs, p = 0.0323; chest, p = 0.0455; forehead, p = 0.0444) compared to pre-test values. During the recovery period, both legs showed a significant and continuous temperature increase (right leg, p = 0.0272; left leg, p = 0.0382), whereas a non-significant drop was noted in the chest and forehead temperatures. Additionally, participants with a lower anaerobic capacity exhibited a higher delta increase in surface leg temperature than participants with higher anaerobic capacities, with a minimal change in surface leg temperature. This is the first study to demonstrate body surface temperature changes following the Wingate anaerobic test. This temperature increase is attributed to the high anaerobic mechanical power outputs achieved by the leg muscles and the time taken for temperature reduction post-exercise.