未经批准:要开发,test,并验证深度学习(DL)工具,该工具改进了先前基于特征的CT图像处理骨矿物质密度(BMD)算法,并将其与手动参考标准进行比较。
未经评估:这个单中心,回顾性,符合健康保险可移植性和责任法案的研究包括11035例患者的腹部CT扫描手动L1小梁Hounsfield单位测量(平均年龄,58岁±12[SD];6311名女性)作为参考标准。然后在此CT队列中使用先前验证的基于特征的图像处理工具和新的DL工具进行自动水平选择和L1小梁感兴趣区域(ROI)放置。评估了总体技术成功率和与手动参考标准的一致性。
UNASSIGNED:在此异质患者队列中,DL工具的总体成功率显着高于较旧的图像处理BMD算法(99.3%vs89.4%,P<.001)。使用此DL工具,单的最接近的Hounsfield单位中值-,三-,七层椎体ROI在手动参考标准Hounsfield单位值的5%以内,为35.1%,56.9%,85.8%的扫描;56.6%的扫描量在10%以内,75.6%,扫描量为92.9%;76.5%的扫描量在25%以内,89.3%,和97.1%的扫描,分别。从单层方法观察到骨质疏松评估的敏感性和特异性的权衡(敏感性,39.4%;特异性,98.3%)到多切片方法的最小值(对于七个连续切片;灵敏度,71.3%和特异性,94.6%)。
UNASSIGNED:新的DLBMD工具比旧的基于功能的图像处理工具显示出更高的成功率,并且其输出可以针对骨质疏松症评估的更高特异性或敏感性。关键词:CT,CT-定量,腹部/GI,骨架-轴向,脊椎,深度学习,机器学习补充材料可用于本文。©RSNA,2022年。
UNASSIGNED: To develop, test, and validate a deep learning (DL) tool that improves upon a previous feature-based CT image processing bone mineral density (BMD) algorithm and compare it against the manual reference standard.
UNASSIGNED: This single-center, retrospective, Health Insurance Portability and Accountability Act-compliant study included manual L1 trabecular Hounsfield unit measurements from abdominal CT scans in 11 035 patients (mean age, 58 years ± 12 [SD]; 6311 women) as the reference standard. Automated level selection and L1 trabecular region of interest (ROI) placement were then performed in this CT cohort with both a previously validated feature-based image processing tool and a new DL tool. Overall technical success rates and agreement with the manual reference standard were assessed.
UNASSIGNED: The overall success rate of the DL tool in this heterogeneous patient cohort was significantly higher than that of the older image processing BMD algorithm (99.3% vs 89.4%, P < .001). Using this DL tool, the closest median Hounsfield unit values for single-, three-, and seven-slice vertebral ROIs were within 5% of the manual reference standard Hounsfield unit values in 35.1%, 56.9%, and 85.8% of scans; within 10% in 56.6%, 75.6%, and 92.9% of scans; and within 25% in 76.5%, 89.3%, and 97.1% of scans, respectively. Trade-offs in sensitivity and specificity for osteoporosis assessment were observed from the single-slice approach (sensitivity, 39.4%; specificity, 98.3%) to the minimum value of the multislice approach (for seven contiguous slices; sensitivity, 71.3% and specificity, 94.6%).
UNASSIGNED: The new DL BMD tool demonstrated a higher success rate than the older feature-based image processing tool, and its outputs can be targeted for higher specificity or sensitivity for osteoporosis assessment.Keywords: CT, CT-Quantitative, Abdomen/GI, Skeletal-Axial, Spine, Deep Learning, Machine Learning Supplemental material is available for this article. © RSNA, 2022.