To assess the diagnostic performance of dual-energy CT with reconstruction of virtual noncalcium (VNCa) images for the detection of lumbar disk herniation compared with standard CT image reconstruction. Materials and Methods:For this retrospective study, 41 patients (243 intervertebral disks; overall mean age, 68 years; 24 women [mean age, 68 years] and 17 men [mean age, 68 years]) underwent clinically indicated third-generation, dual-source, dual-energy CT and 3.0-T MRI within 2 weeks between March 2017 and January 2018. Six radiologists, blinded to clinical and MRI information, independently evaluated conventional gray-scale dual-energy CT series for the presence and degree of lumbar disk herniation and spinal nerve root impingement. After 8 weeks, readers reevaluated examinations by using color-coded VNCa reconstructions. MRI evaluated by two separate experienced readers, blinded to clinical and dual-energy CT information, served as the standard of reference. Sensitivity and specificity were the primary metrics of diagnostic performance.Results: A total of 112 herniated lumbar disks were depicted at MRI. VNCa showed higher overall sensitivity (612 of 672 [91%] vs 534 of 672 [80%]) and specificity (723 of 786 [92%] vs 665 of 786 [85%]) for detecting lumbar disk herniation compared with standard CT (all comparisons, P , .001). Interreader agreement was excellent for VNCa and substantial for standard CT (k = 0.82 vs 0.67; P , .001). VNCa achieved superior diagnostic confidence, image quality, and noise scores compared with standard CT (all comparisons, P , .001). Conclusion:Color-coded dual-energy CT virtual noncalcium reconstructions show substantially higher diagnostic performance and confidence for depicting lumbar disk herniation compared with standard CT.

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Background: Bone age (BA) assessment performed by artificial intelligence (AI) is of growing interest due to improved accuracy, precision and time efficiency in daily routine. The aim of this study was to investigate the accuracy and efficiency of a novel AI software version for automated BA assessment in comparison to the Greulich-Pyle method. Methods: Radiographs of 514 patients were analysed in this retrospective study. Total BA was assessed independently by three blinded radiologists applying the GP method and by the AI software. Overall and genderspecific BA assessment results, as well as reading times of both approaches, were compared, while the reference BA was defined by two blinded experienced paediatric radiologists in consensus by application of the Greulich-Pyle method. Results: Mean absolute deviation (MAD) and root mean square deviation (RSMD) were significantly lower between AI-derived BA and reference BA (MAD 0.34 years, RSMD 0.38 years) than between reader-calculated BA and reference BA (MAD 0.79 years, RSMD 0.89 years; p < 0.001). The correlation between AI-derived BA and reference BA (r = 0.99) was significantly higher than between reader-calculated BA and reference BA (r = 0.90; p < 0.001). No statistical difference was found in reader agreement and correlation analyses regarding gender (p = 0.241). Mean reading times were reduced by 87% using the AI system. Conclusions: A novel AI software enabled highly accurate automated BA assessment. It may improve efficiency in clinical routine by reducing reading times without compromising the accuracy compared with the Greulich-Pyle method.