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Artificial Intelligence for Thermal Imaging in Medical and Industrial Applications

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Optics and Lasers".

Deadline for manuscript submissions: 20 September 2026 | Viewed by 967

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Guest Editor
School of Electrical Engineering, Afeka Tel Aviv Academic College of Engineering, Tel Aviv 6910717, Israel
Interests: image processing; thermal imaging; machine learning; disease detection algorithms; biomedical diagnostics
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue focuses on artificial intelligence-driven thermal imaging for medical and industrial applications. Advances in infrared thermography, combined with machine learning and computer vision, have enabled the non-contact, non-invasive monitoring and detection of physiological and structural anomalies. Thermal imaging provides unique spatiotemporal information related to heat distribution, making it a powerful modality for early diagnosis, condition monitoring, and intelligent inspection systems.

This Special Issue is centered on the AI-based analysis of thermal data, including deep learning, machine learning, and multimodal approaches applied to infrared imaging. Particular emphasis is placed on medical diagnostics, physiological monitoring, and industrial inspection, where thermal imaging supports early detection, fault identification, and predictive analysis.

We seek original research and review papers addressing thermal image processing, automated feature extraction, intelligent thermal pattern analysis, and real-time thermographic systems. Topics of interest include medical thermography, non-invasive diagnostic technologies, industrial fault detection, smart sensing systems, and the integration of thermal cameras with embedded and AI-driven platforms. Contributions that demonstrate practical implementations and clinically or industrially relevant applications are particularly encouraged.

Dr. Oshrit A. Hoffer
Guest Editor

Manuscript Submission Information

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Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • thermal imaging
  • infrared thermography
  • AI in thermography
  • medical thermography
  • thermal image analysis
  • non-invasive diagnostics
  • industrial thermal inspection
  • intelligent sensing systems

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Published Papers (1 paper)

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Research

17 pages, 1778 KB  
Article
A Novel POC Modality for Ex Vivo Malignancy Detection Using Thermal Diffusion Analysis: A Case Study of Lung Cancer
by Sharon Gat, Gal Aviram, Amir Yehudayoff, Hen Toledano, Moshe Tshuva, Assaf Gur, Shani Toledano, Amir Onn and Gabriel Polliack
Appl. Sci. 2026, 16(9), 4516; https://doi.org/10.3390/app16094516 - 4 May 2026
Viewed by 643
Abstract
Early detection of malignancy is imperative, yet existing diagnostic approaches struggle to identify small peripheral lesions. This study evaluated a novel imaging modality, heat diffusion analysis, to assess its ability to differentiate between malignant and normal lung tissue. Considering that lung cancer is [...] Read more.
Early detection of malignancy is imperative, yet existing diagnostic approaches struggle to identify small peripheral lesions. This study evaluated a novel imaging modality, heat diffusion analysis, to assess its ability to differentiate between malignant and normal lung tissue. Considering that lung cancer is the leading cause of cancer-related mortality worldwide, lung tumors were induced in mice in a preclinical ex vivo model to evaluate the proposed technology. The HTOScan System was used to analyze the thermal characteristics of 60 sites from excised lungs, including normal and abnormal regions. The algorithm classified pixels as high- or low-risk for malignancy. The HTOScan System demonstrated a high accuracy of 97%, with 94% sensitivity and 98% specificity compared to the gold standard of histopathology. The technology successfully differentiated abnormal from normal tissue ex vivo based on differences in thermal diffusivity. This proof-of-concept study suggests that combining heat diffusion imaging techniques with machine learning algorithms could enable the HTOScan System to identify malignant lesions accurately with high confidence. The technique shows promise as a real-time decision support tool for cancer detection, pending further in vivo validation. This novel functional-imaging approach could improve the identification of peripheral lesions and the guidance of biopsies during bronchoscopy. Full article
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