A Deep Learning Model for Three-Dimensional Determination of Whole Thoracic Vertebral Bone Mineral Density from Noncontrast Chest CT: The Multi-Ethnic Study of Atherosclerosis.

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Tác giả: Matthew Allison, R Graham Barr, David A Bluemke, Matthew Budoff, Chris Dailing, Shadpour Demehri, Elena Ghotbi, Quincy A Hathaway, Hamza Ahmed Ibad, Arta Kasaeian, Joshua G Klein, João A C Lima, Tommy Pan, Wendy Post, Geoffrey H Tison

Ngôn ngữ: eng

Ký hiệu phân loại: 006.8 Virtual reality

Thông tin xuất bản: United States : Radiology , 2025

Mô tả vật lý:

Bộ sưu tập: NCBI

ID: 694282

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Background Recent studies have investigated how deep learning (DL) algorithms applied to CT using two-dimensional (2D) segmentation (sagittal or axial planes) can calculate bone mineral density (BMD) and predict osteoporosis-related outcomes. Purpose To determine whether TotalSegmentator, an nnU-net algorithm, can measure three-dimensional (3D) vertebral body BMD across consistently imaged thoracic levels (T1-T10) at any conventional, noncontrast chest CT examination. Materials and Methods This study is a secondary analysis of a multicenter (

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