2021
DOI: 10.2147/rmhp.s312330
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A Novel Application of Unsupervised Machine Learning and Supervised Machine Learning-Derived Radiomics in Anterior Cruciate Ligament Rupture

Abstract: Purpose We aim to present an unsupervised machine learning application in anterior cruciate ligament (ACL) rupture and evaluate whether supervised machine learning-derived radiomics features enable prediction of ACL rupture accurately. Patients and Methods Sixty-eight patients were reviewed. Their demographic features were recorded, radiomics features were extracted, and the input dataset was defined as a collection of demographic features and radiomics features. The in… Show more

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Cited by 5 publications
(2 citation statements)
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“…While conventional imaging modalities, such as magnetic resonance imaging (MRI) and computed tomography (CT), have been used to evaluate fibrosis in IBD, their limitations in providing quantitative and objective measurements have prompted the exploration of alternative approaches 10 . Radiomics, a rapidly evolving field in medical imaging, has gained significant attention in various clinical domains, where it has shown great potential for predicting treatment response, prognosis, and even guiding personalized therapies 11 . Radiomics refers to the extraction of a large number of quantitative imaging features from medical images, followed by the application of advanced data analysis techniques 12,13 .…”
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confidence: 99%
“…While conventional imaging modalities, such as magnetic resonance imaging (MRI) and computed tomography (CT), have been used to evaluate fibrosis in IBD, their limitations in providing quantitative and objective measurements have prompted the exploration of alternative approaches 10 . Radiomics, a rapidly evolving field in medical imaging, has gained significant attention in various clinical domains, where it has shown great potential for predicting treatment response, prognosis, and even guiding personalized therapies 11 . Radiomics refers to the extraction of a large number of quantitative imaging features from medical images, followed by the application of advanced data analysis techniques 12,13 .…”
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confidence: 99%
“…SVM operates by identifying the optimal hyperplane within the data space to effectively segregate samples belonging to different classes [ 38 , 39 ]. Previous studies, such as the one conducted by Chen et al [ 40 ], also reported the efficacy of SVM classifiers in diagnosing ACL tears. However, his study solely compared the performance of the random forest (RF) and SVM classifiers.…”
Section: Discussionmentioning
confidence: 99%