{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,27]],"date-time":"2026-03-27T04:13:02Z","timestamp":1774584782734,"version":"3.50.1"},"reference-count":93,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2025,8,22]],"date-time":"2025-08-22T00:00:00Z","timestamp":1755820800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Computers"],"abstract":"<jats:p>Prolonged operation of biomedical devices may compromise electronic component integrity due to cyclic thermal stress, thereby impacting both functionality and safety. Regulatory standards require regular inspections, particularly for surgical applications, highlighting the need for efficient and non-invasive diagnostic tools. This study introduces an integrated system that combines finite element models, infrared thermographic analysis, and artificial intelligence to monitor thermal stress in printed circuit boards (PCBs) within biomedical devices. A dynamic thermal model, implemented in COMSOL Multiphysics\u00ae (version 6.2), identifies regions at high risk of thermal overload. The infrared measurements acquired through a FLIR P660 thermal camera provided experimental validation and a dataset for training a hybrid artificial intelligence system. This model integrates deep learning-based U-Net architecture for thermal anomaly segmentation with machine learning classification of heat diffusion patterns. By combining simulation, the proposed system achieved an F1-score of 0.970 for hotspot segmentation using a U-Net architecture and an F1-score of 0.933 for the classification of heat propagation modes via a Multi-Layer Perceptron. This study contributes to the development of intelligent diagnostic tools for biomedical electronics by integrating physics-based simulation and AI-driven thermographic analysis, supporting automatic classification and localisation of thermal anomalies, real-time fault detection and predictive maintenance strategies.<\/jats:p>","DOI":"10.3390\/computers14090344","type":"journal-article","created":{"date-parts":[[2025,8,22]],"date-time":"2025-08-22T13:47:49Z","timestamp":1755870469000},"page":"344","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":40,"title":["Hybrid FEM-AI Approach for Thermographic Monitoring of Biomedical Electronic Devices"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0009-0001-0218-7724","authenticated-orcid":false,"given":"Danilo","family":"Prattic\u00f2","sequence":"first","affiliation":[{"name":"DICEAM Department, \u201cMediterranea\u201d University, 89122 Reggio Calabria, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4821-7810","authenticated-orcid":false,"given":"Domenico De","family":"Carlo","sequence":"additional","affiliation":[{"name":"DICEAM Department, \u201cMediterranea\u201d University, 89122 Reggio Calabria, Italy"}]},{"given":"Gaetano","family":"Silipo","sequence":"additional","affiliation":[{"name":"System House S.r.l. (a Limited Liability Company), Imbrecciato Street136, 00149 Rome, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6846-1784","authenticated-orcid":false,"given":"Filippo","family":"Lagan\u00e0","sequence":"additional","affiliation":[{"name":"Laboratory of Biomedical Applications Technologies and Sensors (BATS), Department of Health Science, \u201cMagna Gr\u00e6cia\u201d University, 88100 Catanzaro, Italy"}]}],"member":"1968","published-online":{"date-parts":[[2025,8,22]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"16","DOI":"10.1016\/j.infoh.2023.10.001","article-title":"Exploring the advent of Medical 4.0: A bibliometric analysis systematic review and technology adoption insights","volume":"1","author":"Akhtar","year":"2024","journal-title":"Inform. Health"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Afolalu, O.O., Akpor, O.A., Afolalu, S.A., and Afolalu, O.F. (2024, January 2\u20134). 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