Sistema de Salut de Catalunya
Institut Català de la Salut
[Eleftheriadis V, Paneta V] R&D Department, Bioemission Technology Solutions, Athens, Greece. [Herance Camacho JR, Paun B, Aparicio C, Loudos G] Grup de Recerca d’Imatge Mèdica Molecular, Vall d’Hebron Institut de Recerca (VHIR), Barcelona, Spain. CIBER-BBN, CIBBIM-Nanomedicine, ISCIII, Barcelona, Spain. Vall d’Hebron Hospital Universitari, Barcelona, Spain. Universitat Autònoma de Barcelona, Bellaterra, Spain. [Venegas V, Marotta M] Health & Biomedicine Department, Leitat Technological Center, Barcelona, Spain. Grup de Recerca de Bioenginyeria, Teràpia Cel·lular i Cirurgia en Malformacions Congènites, Vall d’Hebron Institut de Recerca (VHIR), Barcelona, Spain. CIBBIM-Nanomedicine, Vall d’Hebron Hospital Universitari, Barcelona, Spain. Universitat Autònoma de Barcelona, Bellaterra, Spain
Vall d'Hebron Barcelona Hospital Campus
2024-01-15T13:12:58Z
2024-01-15T13:12:58Z
2023-11
Injuries; Muscles; Radiomics
Lesions; Músculs; Radiòmica
Lesiones; Músculos; Radiómica
Radiomics as a novel quantitative approach to medical imaging is an emerging area in the field of radiology. Artificial intelligence offers promising tools for exploiting and analyzing radiomics. The objective of the present study is to propose a methodology for the design, development, and evaluation of machine learning (ML) models for the prediction of the recovery progress of skeletal muscle injury over time in rats using radiomics. Radiomics were extracted from contrast enhanced computed tomography (CT) data and ML algorithms were trained and compared for their predictive value based on different CT imaging parameters. Ten different ML regression algorithms were tested and the optimal combination of radiomics for each algorithm and CT imaging parameter settings combination was studied. The best ensemble learning model, trained on the 70 kVp, 100 mA imaging parameter dataset, achieved a mean absolute error score of 1.22. The results suggest that radiomics extracted from CT images can be used as input in ML regression algorithms to predict the volume of a skeletal muscle injury in rats. Moreover, the results show that CT imaging settings impact the predictive performance of the ML regression models, indicating that lower values of tube current and peak kilovoltage contribute to more accurate predictions.
10.13039/100010671-European Union’s Horizon Research and Innovation Program (Grant Number: 761031)
Article
Published version
English
Muscul estriat - Ferides i lesions; Aprenentatge automàtic; Tomografia; PHENOMENA AND PROCESSES::Mathematical Concepts::Algorithms::Artificial Intelligence::Machine Learning; ANALYTICAL, DIAGNOSTIC AND THERAPEUTIC TECHNIQUES, AND EQUIPMENT::Diagnosis::Diagnostic Techniques and Procedures::Diagnostic Imaging::Image Interpretation, Computer-Assisted::Tomography, X-Ray Computed; ANATOMY::Musculoskeletal System::Muscles::Muscle, Skeletal; DISEASES::Wounds and Injuries; FENÓMENOS Y PROCESOS::conceptos matemáticos::algoritmos::inteligencia artificial::aprendizaje automático; TÉCNICAS Y EQUIPOS ANALÍTICOS, DIAGNÓSTICOS Y TERAPÉUTICOS::diagnóstico::técnicas y procedimientos diagnósticos::diagnóstico por imagen::interpretación de imágenes asistida por ordenador::tomografía computarizada por rayos X; ANATOMÍA::sistema musculoesquelético::músculos::músculo esquelético; ENFERMEDADES::heridas y lesiones
IEEE
IEEE Transactions on Radiation and Plasma Medical Sciences;7(8)
https://doi.org/10.1109/TRPMS.2023.3291848
info:eu-repo/grantAgreement/EC/H2020/761031
Attribution-NonCommercial-NoDerivatives 4.0 International
http://creativecommons.org/licenses/by-nc-nd/4.0/
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