The study’s analytical framework was validated using two distinct movable cultural heritage specimens. Both analyzed samples exhibit a complex layered structure, making them comparable to the composite materials investigated in the engineering field through thermography. Each artifact was prepared with...

A new diagnostic accuracy study demonstrates how the combination of smartphone-based thermography and artificial intelligence (AI) could revolutionize routine screening for diabetic foot ulcers (DFUs). This technology offers a scalable, objective alternative to traditional manual exams, which are often episodic and highly dependent on examiner expertise. The Tech: TFScan The study evaluated TFScan, a system comprising a portable thermal sensor and a smartphone app that uses computer vision to analyze plantar images.

Background:To synthesize evidence on how medical thermography, integrated with artificial intelligence (AI), blockchain, 5G (5th Generation mobile networks), and Internet of Things (IoT), enhanc

**Abstract:** This paper introduces a novel, real-time defect classification methodology for SiC sintering processes using a Bayesian network (BN) fusion of acoustic emission (AE) and infrared (IR) thermography data. Current quality control methods rely on post-sintering destructive testing, which is costly and delays feedback loops. Our system leverages established AE and IR technologies, integrating them through a sophisticated BN framework that dynamically adapts to process variations.

Infrared inspections help identify hidden electrical, moisture, and heat issues in office buildings, warehouses, and retail spaces before costly failures occur.

Jove

Why Commercial Properties Benefit from Infrared Inspection Services

PhD project exploring how heatwaves and climate extremes affect coastal rock slope stability in the UK. Using UAV LiDAR, UAV infrared thermography, and in-situ sensors, the research will monitor therm...

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**Abstract:** This paper introduces a novel system for automated corrosion detection and quantification in bridge components during mobile inspection operations. Leveraging multi-spectral infrared (MSIR) thermography coupled with a custom-trained Convolutional Neural Network (CNN) optimized for anomaly detection, the system provides rapid, accurate, and non-destructive assessment of corrosion severity. The system is designed for seamless integration with mobile inspection vehicles, significantly reducing inspection time and improving data consistency compared to traditional manual methods.

Background: Diabetic foot complications are among the most severe and costly outcomes associated with diabetes, with high prevalence particularly in the Middle East and North Africa region. Current screening tools are often limited by subjectivity, invasiveness, or scalability challenges, underscoring the need for innovative approaches. Objective: This multicenter study aimed to evaluate the performance of an artificial intelligence (#AI) (AI)–powered thermographic system, Thermal Foot Scan (TFScan), in identifying patients at elevated risk of diabetic foot complications through noninvasive temperature profiling. Methods: A multicenter cross-sectional analysis of deidentified routine screening data across 4 regions in Saudi Arabia was conducted enrolling 1120 individuals with diabetes. Participants underwent thermal imaging using a smartphone-compatible infrared camera with AI algorithms analyzing angiosomal temperature patterns and asymmetries. Risk was stratified into 4 categories (very low, low, moderate, and high). Associations between TFScan classifications and clinical risk factors, symptoms of neuropathy, and thermal abnormalities were assessed. Results: While 90.7% (1016/1120) of the participants were classified as very low or low risk, 9.3% (104/1120) were identified as moderate or high risk. This higher-risk group exhibited significantly greater prevalence of key diabetic complications (P

**Abstract:** This paper presents a novel approach for automated thermal bridge detection and quantification in building envelopes using infrared thermography (IRT). Traditional methods suffer from subjectivity and time-consuming manual inspection. Our solution, utilizing a Deep Feature Fusion Network (DFFN) coupled with Bayesian Inference, achieves enhanced accuracy, robustness, and scalability for identifying and quantifying thermal bridges, thereby enabling efficient building energy audits and retrofitting decisions.

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