A deep learning algorithm for detecting lytic bone lesions of multiple myeloma on CT

Background Whole-body low-dose CT is the recommended initial imaging modality to evaluate bone destruction as a result of multiple myeloma. Accurate interpretation of these scans to detect small lytic bone lesions is time intensive. A functional deep learning) algorithm to detect lytic lesions on CT...

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Veröffentlicht in:	Skeletal radiology Jg. 52; H. 1; S. 91 - 98
Hauptverfasser:	Faghani, Shahriar, Baffour, Francis I., Ringler, Michael D., Hamilton-Cave, Matthew, Rouzrokh, Pouria, Moassefi, Mana, Khosravi, Bardia, Erickson, Bradley J.
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Berlin/Heidelberg Springer Berlin Heidelberg 01.01.2023 Springer Springer Nature B.V
Schlagworte:	Algorithms Bone imaging Bone lesions Computed tomography CT imaging Data mining Deep Learning Diagnostic imaging Humans Image processing Image segmentation Imaging Lesions Machine learning Medical imaging Medical imaging equipment Medicine Medicine & Public Health Monoclonal gammopathy Multiple myeloma Multiple Myeloma - diagnostic imaging Multiple Myeloma - pathology Nuclear Medicine Orthopedics Osteolysis Pathology Radiology Scientific Article Tomography, X-Ray Computed - methods Deep learning Bone segmentation Whole-body low-dose CT Lesion detection Multiple myeloma
ISSN:	0364-2348, 1432-2161, 1432-2161
Online-Zugang:	Volltext
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