Automated Cobb Angle Measurement in Adolescent Idiopathic Scoliosis: Validation of a Previously-Published Deep Learning Method

被引：1

作者：

Yan, Shi ^{[1
]}

Constant, Caroline ^{[2
]}

Ramazanian, Taghi ^{[3
]}

Kremers, Hilal Maradit ^{[3
]}

Larson, A. Noelle ^{[4
]}

机构：

[1] Mayo Clin, Dept AI & Informat, Rochester, MN 55905 USA

[2] AO Res Inst, Davos, Switzerland

[3] Mayo Clin, Dept Quantitat Hlth Sci, Rochester, MN USA

[4] Mayo Clin, Dept Orthoped Surg, Rochester, MN USA

来源：

2022 IEEE 10TH INTERNATIONAL CONFERENCE ON HEALTHCARE INFORMATICS (ICHI 2022) | 2022年

关键词：

cobb angle; scoliosis; deep learning;

D O I：

10.1109/ICHI54592.2022.00085

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

The severity of scoliosis and surgical decisions are determined based on accurate measurement of the Cobb angle of the spine. There are several previously published deep learning models for automated measurement of Cobb angle, but none are externally validated in severe scoliosis patients. We evaluated the external performance of a previously published deep learning method for Cobb angle measurement in 2278 full-spine X-rays of 860 severe scoliosis patients. The model performed poorly and missed several vertebrae when labelling landmarks. Findings underscore the importance of external validation studies to assess model performance in patient subgroups with varying levels of scoliosis severity.

引用

页码：495 / 496

页数：2