Della Monaca, Sara; Maltar-Strmečki, Nadica; Quattrini, Maria Cristina; Bortolin, Emanuela (2026) AI-driven triage classification at the 1 Gy threshold using dietary supplements and portable OSL dosimetry. Radiation Protection Dosimetry . ISSN 0144-8420
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Abstract
In radiological emergency scenarios, the rapid distinction between individuals exposed below or above the 1 Gy triage threshold is essential for effective medical sorting and optimized resource allocation. This study proposes a binary classification framework at the 1 Gy threshold that combines Optically Stimulated Luminescence (OSL) measurements of commercial magnesium-based dietary supplements with supervised machine learning (ML) algorithms, including Logistic Regression, Support Vector Machines, Decision Trees, Random Forest, and XGBoost. A dataset of 355 samples exposed to different radiation doses was analyzed, and model performance was evaluated using cross-validation and multiple statistical metrics. The resulting framework was implemented into a lightweight, browser-based application to provide real-time predictions and support decision-making in field operations. The findings demonstrate that integrating physical dosimetry with ML enables rapid and scalable classification relative to the 1Gy threshold and offers a practical tool to enhance public health response during radiological incidents.
| Item Type: | Article | ||||||||
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| Additional Information: | This is a pre-copyedited, author-produced version of an article accepted for publication in Radiation Protection Dosimetry following peer review. The version of record Della Monaca, Sara; Maltar-Strmečki, Nadica; Quattrini, Maria Cristina; Bortolin, Emanuela (2026) AI-driven triage classification at the 1 Gy threshold using dietary supplements and portable OSL dosimetry. Radiation Protection Dosimetry is available online at: https://academic.oup.com/rpd/advance-article-abstract/doi/10.1093/rpd/ncag053/8697280 | ||||||||
| Uncontrolled Keywords: | retrospective dosimetry; OSL dosimetry; supervised machine learning; dietary supplements | ||||||||
| Subjects: | NATURAL SCIENCES > Physics NATURAL SCIENCES > Chemistry BIOMEDICINE AND HEALTHCARE > Public Health and Health Care |
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| Divisions: | Division of Physical Chemistry | ||||||||
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| Depositing User: | Nadica Maltar Strmečki | ||||||||
| Date Deposited: | 02 Jun 2026 07:33 | ||||||||
| URI: | https://fulir.irb.hr:/id/eprint/12000 | ||||||||
| DOI: | 10.1093/rpd/ncag053 |
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