Automatically measuring the Cobb angle and screening for scoliosis on chest radiograph with a novel artificial intelligence method.

OBJECTIVES

To establish an automatic approach for the measurement of the Cobb angle and the diagnosis of scoliosis on chest radiograph.

METHODS

We developed an artificial intelligence (AI) automatic program which contained a supervised learning module and an inference module. After the filtering and pre-processing process, 96 images from the Shenzhen chest X-ray set were used for training with the supervised learning module, and 491 test images were separately gauged by the AI and the corresponding manual methods. The results of the two methods were further compared through statistical analyses.

RESULTS

Among the test images, 6068 (99.49%) vertebral bodies were identified within the deviation of one vertebral segment. The value difference between the Cobb angle obtained by the AI program and that measured by specialists was 0.4020±0.8703. The intraclass correlation coefficient of 0.915 indicated the strong agreement. AI scoliosis diagnosis achieved an accuracy of 98.37%, with a specificity of 98.73%, a sensitivity of 88.24% and a kappa coefficient of 0.781. And the area under the receiver operating characteristic curve of 0.979 confirmed the consistency of the two methods in diagnosis.

CONCLUSIONS

We developed a novel automatic AI method with the abilities to measure the Cobb angle, and to identify the approximate vertebral segment and diagnosis of scoliosis on chest radiograph. The results suggest that this method might be a promising alternative strategy for scoliosis screening on chest radiograph and worth further investigation.