The application of deep learning methods in knee joint sports injury diseases

The application of deep learning methods in knee joint sports injury diseases
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DOI:
10.1080/21681163.2023.2261554
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发表时间:
2023-09
期刊:
Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization
影响因子:
--
通讯作者:
Yeqiang Luo;Jing Liang;Shanghui Lin;Tian Bai;Lingchuang Kong;Yan Jin;Xin Zhang;Baofeng Li;Bei Chen
Yeqiang Luo;Jing Liang;Shanghui Lin;Tian Bai;Lingchuang Kong;Yan Jin;Xin Zhang;Baofeng Li;Bei Chen
中科院分区:
其他
文献类型:
--
作者:
Yeqiang Luo;Jing Liang;Shanghui Lin;Tian Bai;Lingchuang Kong;Yan Jin;Xin Zhang;Baofeng Li;Bei Chen

文献摘要

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摘要深度学习是机器学习的一个强大的分支,它为疾病诊断提供了一种很有前途的新方法。然而,用于检测前交叉韧带的深度学习仍然局限于评估是否有损伤。深度学习模型的精度不高,参数复杂。在这项研究中,我们开发了一个基于ResNet-18的深度学习模型来检测ACL状况。结果表明,我们提出的模型和两个骨科医生和放射科医生在诊断ACL条件之间没有显着差异。
ABSTRACT Deep learning is a powerful branch of machine learning, which presents a promising new approach for diagnose diseases. However, the deep learning for detecting anterior cruciate ligament still limits to the evaluation of whether there are injuries. The accuracy of the deep learning model is not high, and the parameters are complex. In this study, we have developed a deep learning model based on ResNet-18 to detect ACL conditions. The results suggest that there is no significant difference between our proposed model and two orthopaedic surgeons and radiologists in diagnosing ACL conditions.