Prediction of bone mineral density from computed tomography: application of deep learning with a convolutional neural network

Objectives To investigate whether a deep learning model can predict the bone mineral density (BMD) of lumbar vertebrae from unenhanced abdominal computed tomography (CT) images. Methods In this Institutional Review Board–approved retrospective study, patients who received both unenhanced CT examinat...

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Veröffentlicht in:	European radiology 2020-06, Vol.30 (6), p.3549-3557
Hauptverfasser:	Yasaka, Koichiro, Akai, Hiroyuki, Kunimatsu, Akira, Kiryu, Shigeru, Abe, Osamu
Format:	Artikel
Sprache:	eng
Schlagworte:	Abdomen Abdomen - diagnostic imaging Absorptiometry, Photon Adult Aged Area Under Curve Artificial intelligence Artificial neural networks Biomedical materials Bone Density Bone mineral density Computed tomography Correlation coefficient Correlation coefficients Datasets Deep Learning Diagnostic Radiology Diagnostic systems Dual energy X-ray absorptiometry Female Humans Imaging Imaging Informatics and Artificial Intelligence Internal Medicine Interventional Radiology Lumbar Vertebrae - diagnostic imaging Machine learning Male Medical imaging Medicine Medicine & Public Health Middle Aged Neural networks Neuroradiology Osteoporosis Osteoporosis - diagnostic imaging Performance evaluation Radiology Reproducibility of Results Retrospective Studies ROC Curve Tomography Tomography, X-Ray Computed Ultrasound Vertebrae
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