Computer-Aided Cobb Measurement Based on Automatic Detection of Vertebral Slopes Using Deep Neural Network

Abstract

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.

Evaluation of a computer-aided method for measuring the Cobb angle on chest X-rays

“…Hence, the manual measurement was performed on digital chest X-rays using PACS in the current study. The intra-and inter-observer variability of manual measurement on digital chest X-rays performed by radiologists was comparable with that on printed radiographs performed by orthopedic surgeons in previous studies [9,16,17] (Table 6). Even though radiologists received support from PACS, the variability that was caused by the defect in classic measurement of the Cobb angle still remained [17].…”

“…Intraclass correlation coefficients (ICCs) with 95% CIs were used to analyze the reliability; ICC < 0.70, 0.70-0.79, 0.80-0.89, and 0.9-0.99 were considered as poor, fair, good, and excellent reliability, respectively [6]. For variability analysis, the mean absolute difference (MAD) of the two measurements was calculated [9]. Using the mean value of Cobb angles in manual measurements > 10° as a reference standard for scoliosis, the sensitivity, specificity, accuracy, and positive and negative predictive values of the computer-aided method for diagnosing scoliosis were calculated.…”

“…Therefore, the reliability and accuracy are crucial with respect to the Cobb angle measurement. Manual measurement on printed radiographs can be easily performed and regarded as the standard for reliability assessment [9,15]. In some cases, the endplates of vertebral bodies are difficult to recognize on printed chest X-rays.…”

“…Wu et al [8] proposed a multi-view correlation network (MVC-Net) that allowed automatic assessment of the spinal curvature on anteroposterior and lateral X-ray views through joint multi-view input feature learning and explicit reinforcement of reciprocal relationships between the spinal landmark and Cobb angle; however, the MVC-Net might not be ideally suited for elderly patients with scoliosis because the spinal landmark located in the four vertices of each vertebral body would be varied with the formation of marginal osteophytes. Zhang et al [9] developed a computer-aided method using a deep neural network that still requires manual intervention, such as assignment of vertebral patches, and was not reliable to measure Cobb angle for in vivo radiographs.…”

Intelligent Precise Goniometric System of Analysis of Spectral Distribution Intensities for Definition of Chemical Composition of Metal-Containing Substances

“…Use of ANN is also well known in the method for evaluation of generator rotation angle and its speed, in particular, for stability analysis and control of transient processes in real-time [16]. It is also known that the system of automated Cobb angle measuring is based on ANN for scoliosis examination [17]. The use of ANN leads to the rise of productivity, reliability and accuracy of work performed.…”