Objective To develop a computer-aided method that reduces the variability of Cobb angle measurement for scoliosis assessment. Methods A deep neural network (DNN) was trained with vertebral patches extracted from spinal model radiographs. The Cobb angle of the spinal curve was calculated automatically from the vertebral slopes predicted by the DNN. Sixty-five in vivo radiographs and 40 model radiographs were analyzed. An experienced surgeon performed manual measurements on the aforementioned radiographs. Two examiners used both the proposed and the manual measurement methods to analyze the aforementioned radiographs. Results For model radiographs, the intraclass correlation coefficients were greater than 0.98, and the mean absolute differences were less than 3°. This indicates that the proposed system showed high repeatability for measurements of model radiographs. For the in vivo radiographs, the reliabilities were lower than those from the model radiographs, and the differences between the computer-aided measurement and the manual measurement by the surgeon were higher than 5°. Conclusion The variability of Cobb angle measurements can be reduced if the DNN system is trained with enough vertebral patches. Training data of in vivo radiographs must be included to improve the performance of DNN. Significance Vertebral slopes can be predicted by DNN. The computer-aided system can be used to perform automatic measurements of Cobb angle, which is used to make reliable and objective assessments of scoliosis.

Compared with the documented results, variability of the Cobb measurement is reduced by using the developed computer-aided method. This method can help orthopedic surgeons measure the Cobb angle more reliably during scoliosis clinics.

The present study aimed to evaluate the diagnostic value of 64-slice spiral multivariate computed tomography (CT) combined with dynamic contrast-enhanced scanning for benign and malignant solitary pulmonary nodules (SPNs). A total of 93 patients with SPN as diagnosed by CT were included. All these patients were subjected to routine and dynamic enhancement CT scanning. After reconstruction, the morphological characteristics following dynamic enhancement were analyzed, and compared for the benign and malignant SPN cases. The incidences of lobulation, spicular sign, pleural indentation and vacuole sign in the malignant SPN group were significantly higher compared with the benign SPN group. During the dynamic enhancement scanning, the CT values at all the time points for the inflammatory and malignant SPN groups were significantly higher than the benign SPN group. No significant differences were observed in the dynamic enhancement CT values at 30, 60, 90 and 120 sec between the inflammatory, and malignant SPN groups. However, in the inflammatory SPN group, the dynamic enhancement CT values at 300 and 540 sec were significantly lower than the malignant SPN group. Notably, the diagnostic accordance rate for the morphological signs combined with dynamic enhancement diagnosis was significantly higher than the morphological signs alone. The 64-slice spiral CT morphological signs combined with dynamic enhancement detection can be more effective for the differential diagnosis of benign and malignant SPN, which may provide potent evidence for the early clinical treatment.

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