[Application of Deep Learning to Diagnose and Classify Adolescent Idiopathic Scoliosis].

Q4 Medicine
Kunjie Xie, Wei Lei, Suping Zhu, Yaopeng Chen, Jincong Lin, Yi Li, Yabo Yan
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引用次数: 0

Abstract

A deep learning-based model for automatic diagnosis and classification of adolescent idiopathic scoliosis has been constructed. This model mainly included key points detection and Cobb angle measurement. 748 full-length standing spinal X-ray images were retrospectively collected, of which 602 images were used to train and validate the model, and 146 images were used to test the model performance. The results showed that the model had good diagnostic and classification performance, with an accuracy of 94.5%. Compared with experts' measurement, 94.9% of its Cobb angle measurement results were within the clinically acceptable range. The average absolute difference was 2.1°, and the consistency was also excellent (r2≥0.9552, P<0.001). In the future, this model could be applied clinically to improve doctors' diagnostic efficiency.

[应用深度学习对青少年特发性脊柱侧凸进行诊断和分类]。
构建了一个基于深度学习的青少年特发性脊柱侧弯症自动诊断和分类模型。该模型主要包括关键点检测和 Cobb 角度测量。研究人员回顾性收集了748张全长立位脊柱X光图像,其中602张用于训练和验证模型,146张用于测试模型性能。结果表明,该模型具有良好的诊断和分类性能,准确率达 94.5%。与专家的测量结果相比,94.9% 的 Cobb 角测量结果在临床可接受的范围内。平均绝对差值为 2.1°,一致性也很好(r2≥0.9552,P<0.05)。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
中国医疗器械杂志
中国医疗器械杂志 Medicine-Medicine (all)
CiteScore
0.40
自引率
0.00%
发文量
8086
期刊介绍:
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