ORIGINAL RESEARCH article
Front. Public Health
Sec. Occupational Health and Safety
Volume 13 - 2025 | doi: 10.3389/fpubh.2025.1584136
This article is part of the Research TopicMineral Particles and Fibers and Human Health Risks: State-of-the-Art in Characterization, Analysis, Tissue Analytics, Exposure Thresholds for Risk, Epidemiology, and Risk Assessment for Science-Based Regulation and Disease Prevention and Implications for Occupational Health and SafetyView all 16 articles
Application of Artificial Intelligence in the Analysis of Asbestos Fibers
Provisionally accepted
- RJ Lee Group (United States), Monroeville, United States
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Automated asbestos fiber detection and identification has been the goal of asbestos microscopists for decades. The advent of inexpensive memory, fast digital processing, machine learning, and microscope automation provide the enabling platform for success. This paper will review recent developments in fiber detection and identification by PCM and SEM and will present recent progress in employing artificial intelligence in the TEM classification of asbestos and non-asbestos amphiboles in the evaluation of elongated minerals in raw materials. To date, this project has been self-funded.
Keywords: Asbestos, Automation, IDENTIFICATION, artificial intelligence, Amphibole
Received: 26 Feb 2025; Accepted: 03 Jun 2025.
Copyright: © 2025 Lee, Van Orden, Blanda, Mihalick, Bickford and Metch. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Drew R Van Orden, RJ Lee Group (United States), Monroeville, United States
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