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Sarcopenia prediction using shear-wave elastography, grayscale ultrasonography, and clinical information with machine learning fusion techniques: feature-level fusion vs. score-level fusion
DC Field | Value | Language |
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dc.contributor.author | 이영한 | - |
dc.contributor.author | 오강록 | - |
dc.date.accessioned | 2024-03-22T07:06:51Z | - |
dc.date.available | 2024-03-22T07:06:51Z | - |
dc.date.issued | 2024-02 | - |
dc.identifier.uri | https://ir.ymlib.yonsei.ac.kr/handle/22282913/198682 | - |
dc.description.abstract | This study aimed to develop and evaluate a sarcopenia prediction model by fusing numerical features from shear-wave elastography (SWE) and gray-scale ultrasonography (GSU) examinations, using the rectus femoris muscle (RF) and categorical/numerical features related to clinical information. Both cohorts (development, 70 healthy subjects; evaluation, 81 patients) underwent ultrasonography (SWE and GSU) and computed tomography. Sarcopenia was determined using skeletal muscle index calculated from the computed tomography. Clinical and ultrasonography measurements were used to predict sarcopenia based on a linear regression model with the least absolute shrinkage and selection operator (LASSO) regularization. Furthermore, clinical and ultrasonography features were combined at the feature and score levels to improve sarcopenia prediction performance. The accuracies of LASSO were 70.57 ± 5.00-81.54 ± 4.83 (clinical) and 69.00 ± 4.52-69.73 ± 5.47 (ultrasonography). Feature-level fusion of clinical and ultrasonography (accuracy, 70.29 ± 6.63 and 83.55 ± 4.32) showed similar performance with clinical features. Score-level fusion by AdaBoost showed the best performance (accuracy, 73.43 ± 6.57-83.17 ± 5.51) in the development and evaluation cohorts, respectively. This study might suggest the potential of machine learning fusion techniques to enhance the accuracy of sarcopenia prediction models and improve clinical decision-making in patients with sarcopenia. | - |
dc.description.statementOfResponsibility | open | - |
dc.format | application/pdf | - |
dc.language | English | - |
dc.publisher | Nature Publishing Group | - |
dc.relation.isPartOf | SCIENTIFIC REPORTS | - |
dc.rights | CC BY-NC-ND 2.0 KR | - |
dc.subject.MESH | Elasticity Imaging Techniques* / methods | - |
dc.subject.MESH | Healthy Volunteers | - |
dc.subject.MESH | Humans | - |
dc.subject.MESH | Quadriceps Muscle | - |
dc.subject.MESH | Sarcopenia* / diagnostic imaging | - |
dc.subject.MESH | Ultrasonography / methods | - |
dc.title | Sarcopenia prediction using shear-wave elastography, grayscale ultrasonography, and clinical information with machine learning fusion techniques: feature-level fusion vs. score-level fusion | - |
dc.type | Article | - |
dc.contributor.college | College of Medicine (의과대학) | - |
dc.contributor.department | Dept. of Radiology (영상의학교실) | - |
dc.contributor.googleauthor | Jisook Yi | - |
dc.contributor.googleauthor | Seok Hahn | - |
dc.contributor.googleauthor | Kangrok Oh | - |
dc.contributor.googleauthor | Young Han Lee | - |
dc.identifier.doi | 10.1038/s41598-024-52614-2 | - |
dc.contributor.localId | A02967 | - |
dc.relation.journalcode | J02646 | - |
dc.identifier.eissn | 2045-2322 | - |
dc.identifier.pmid | 38307965 | - |
dc.contributor.alternativeName | Lee, Young Han | - |
dc.contributor.affiliatedAuthor | 이영한 | - |
dc.citation.volume | 14 | - |
dc.citation.number | 1 | - |
dc.citation.startPage | 2769 | - |
dc.identifier.bibliographicCitation | SCIENTIFIC REPORTS, Vol.14(1) : 2769, 2024-02 | - |
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