Gradient-enhanced neural network and extreme gradient boosting modeling for the prediction of the 3D bone mineral density distribution from 2D-DXA scans

This study aims to predict the volumetric bone mineral density (BMD) distribution from a dual-energy X-ray absorptiometry (DXA) scan. By employing machine learning, this study bridges the gap between DXA and computed tomography (CT) in terms of volumetric bone assessment, suggesting an approach for...

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Veröffentlicht in:	Osteoporosis and Sarcopenia Jg. 11; H. 3; S. 98 - 106
Hauptverfasser:	Seo, Jiin, Quagliato, Luca, Chung, Yoon-Sok, Lee, Taeyong
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Netherlands Elsevier B.V 01.09.2025 Elsevier 대한골다공증학회
Schlagworte:	2D-3D mapping Bone mineral density (BMD) prediction Dual-energy X-Ray absorptiometry (DXA) Endocrinology and Metabolism Extreme gradient boosting (XGB) Gradient-enhanced neural network (GENN) 정형외과학 2D-3D mapping Bone mineral density (BMD) prediction Extreme gradient boosting (XGB) Gradient-enhanced neural network (GENN) Dual-energy X-Ray absorptiometry (DXA)
ISSN:	2405-5255, 2405-5263, 2405-5263
Online-Zugang:	Volltext
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