Artificial Intelligence in IR Thermal Imaging and Sensing for Medical Applications

Area of Science:

• Medical Imaging
• Biomedical Engineering
• Artificial Intelligence in Medicine

Background:

• Infrared (IR) thermal imaging has evolved significantly since 1960, offering non-invasive diagnostic capabilities.
• Traditional IR thermography faces challenges in image quality and complex data interpretation.
• The integration of advanced computational methods is crucial for unlocking the full potential of IR thermal imaging in healthcare.

Purpose of the Study:

• To present a comprehensive review of IR thermal imaging technologies and their applications in medical diagnostics from 1960 to 2024.
• To explore the recent advancements and novel applications of artificial intelligence (AI) and machine learning (ML) in analyzing thermal images.
• To discuss how AI-driven approaches can enhance diagnostic accuracy and treatment strategies in medical applications.

Main Methods:

• Review of scientific literature on IR thermal imaging in medicine (1960-2024).
• Analysis of current artificial intelligence (AI) and machine learning (ML) techniques applied to thermal image processing.
• Examination of AI applications for image enhancement, artifact removal, and feature localization.

Main Results:

• AI significantly improves thermal image quality through denoising and super-resolution algorithms.
• AI facilitates object detection and localization of facial and characteristic features in thermal images.
• Machine learning methods enable complex matching of diagnostic symptoms for enhanced medical analysis.

Conclusions:

• AI and ML are transforming IR thermography, leading to more accurate and efficient medical diagnostics.
• The application of AI in IR thermal imaging promises improved patient outcomes and personalized medical treatments.
• Continued research in AI for IR thermography is essential for future advancements in non-invasive medical diagnostics.