Machine learning prediction of effective radiation doses in various computed tomography applications: a virtual human phantom study

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dc.contributor.authorTanyıldızı-Kökkülünk, Handan
dc.date.accessioned2025-07-04T06:55:17Z
dc.date.available2025-07-04T06:55:17Z
dc.date.issued2025
dc.departmentMeslek Yüksekokulları, Sağlık Hizmetleri Meslek Yüksekokulu, Radyoterapi Programı
dc.description.abstractObjectives: In this work, it was aimed to employ machine learning (ML) algorithms to accurately forecast the radiation doses for phantoms while accounting for the most popular CT protocols. Methods: A cloud-based software was utilized to calculate the effective doses from different CT protocols. To simulate a range of adult patients with different weights, eight entire body mesh-based computational phantom sets were used. The head, neck, and chest-abdomen-pelvis CT scan characteristics were combined to create a dataset with 33 rows for each phantom and 792 rows total. At the ML stage, linear (LR), random forest (RF) and support vector regression (SVR) were used. Mean absolute error, mean squared error and accuracy were used to evaluate the performances. Results: The female phantoms received higher doses (7.8 %) than males. Furthermore, an average of 11 % more dose was taken to the normal weight phantom than to the overweight, the overweight in comparison to the obese I, and the obese I in comparison to the obese II. Among the ML algorithms, the LR showed 0 error rate and 100 % accuracy in predicting CT doses. Conclusions: The LR was shown to be the best approach out of those used in the ML estimation of CT-induced doses.
dc.identifier.citationTanyildizi-Kokkulunk, H. (2025). Machine learning prediction of effective radiation doses in various computed tomography applications: a virtual human phantom study. Biomedical Engineering/Biomedizinische Technik, (0).
dc.identifier.doi10.1515/bmt-2024-0620
dc.identifier.issn0013-5585
dc.identifier.issn1862-278X
dc.identifier.pmid40196902
dc.identifier.scopus2-s2.0-105002602219
dc.identifier.scopusqualityQ3
dc.identifier.urihttps://hdl.handle.net/20.500.12939/5793
dc.identifier.wosWOS:001461226400001
dc.identifier.wosqualityQ3
dc.indekslendigikaynakPubMed
dc.institutionauthorTanyıldızı-Kökkülünk, Handan
dc.language.isoen
dc.relation.ispartofBiomedizinische Technik
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.subjectlineer regression
dc.subjectmachine learning
dc.subjectradiation dose
dc.subjectrandom forest
dc.subjectsupport vector machine
dc.titleMachine learning prediction of effective radiation doses in various computed tomography applications: a virtual human phantom study
dc.typeArticle

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