Literature DB >> 26288884

A Computer-aided Method for Improving the Reliability of Lenke Classification for Scoliosis.

Junhua Zhang, Hongjian Li, Liang Lv, Xinllng Shi, Fei Guo, Yufeng Zhang.   

Abstract

Classification of the spinal curve pattern is crucial for assessment and treatment of scoliosis. We developed a computer-aided system to improve the reliability of three components of the Lenke classification. The system semi-automatically measured the Cobb angles and identified the apical lumbar vertebra and its pedicles on digitized radiographs. The system then classified the curve type, lumbar modifier, and thoracic sagittal modifier of the Lenke classification based on the computerized measurements and identifications. The system was tested by five operators for 62 scoliotic cases. The kappa statistic was used to assess the reliability. With the aid of computer, the average intra- and interobserver kappa values were improved to 0.89 and 0.81 for the curve type, to 0.83 and 0.81 for the lumbar modifier, and to 0.94 and 0.92 for the sagittal modifier of the Lenke classification, respectively, relative to the classification by two of the operators without the aid of computer. Results indicate that the computerized system can improve reliability for all three components of the Lenke classification.

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Year:  2015        PMID: 26288884     DOI: 10.1260/2040-2295.6.2.145

Source DB:  PubMed          Journal:  J Healthc Eng        ISSN: 2040-2295            Impact factor:   2.682


  3 in total

Review 1.  Classifications in Brief: The Lenke Classification for Adolescent Idiopathic Scoliosis.

Authors:  Casey Slattery; Kushagra Verma
Journal:  Clin Orthop Relat Res       Date:  2018-11       Impact factor: 4.176

2.  Comparison of manual versus automated measurement of Cobb angle in idiopathic scoliosis based on a deep learning keypoint detection technology.

Authors:  Yu Sun; Yaozhong Xing; Zian Zhao; Xianglong Meng; Gang Xu; Yong Hai
Journal:  Eur Spine J       Date:  2021-10-30       Impact factor: 2.721

3.  A Computer-Aided Detection System for Digital Chest Radiographs.

Authors:  Juan Manuel Carrillo-de-Gea; Ginés García-Mateos; José Luis Fernández-Alemán; José Luis Hernández-Hernández
Journal:  J Healthc Eng       Date:  2016       Impact factor: 2.682

  3 in total

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