Liawrungrueang Wongthawat, Han Inbo, Cholamjiak Watcharaporn, Sarasombath Peem, Riew K Daniel
Department of Orthopaedics, School of Medicine, University of Phayao, Phayao, Thailand.
Department of Neurosurgery, CHA Bundang Medical Center, CHA University School of Medicine, Seongnam, Korea.
Neurospine. 2024 Sep;21(3):833-841. doi: 10.14245/ns.2448580.290. Epub 2024 Sep 30.
To develop and evaluate a technique using convolutional neural networks (CNNs) for the computer-assisted diagnosis of cervical spine fractures from radiographic x-ray images. By leveraging deep learning techniques, the study might potentially lead to improved patient outcomes and clinical decision-making.
This study obtained 500 lateral radiographic cervical spine x-ray images from standard open-source dataset repositories to develop a classification model using CNNs. All the images contained diagnostic information, including normal cervical radiographic images (n=250) and fracture images of the cervical spine fracture (n=250). The model would classify whether the patient had a cervical spine fracture or not. Seventy percent of the images were training data sets used for model training, and 30% were for testing. Konstanz Information Miner (KNIME)'s graphic user interface-based programming enabled class label annotation, data preprocessing, CNNs model training, and performance evaluation.
The performance evaluation of a model for detecting cervical spine fractures presents compelling results across various metrics. This model exhibits high sensitivity (recall) values of 0.886 for fractures and 0.957 for normal cases, indicating its proficiency in identifying true positives. Precision values of 0.954 for fractures and 0.893 for normal cases highlight the model's ability to minimize false positives. With specificity values of 0.957 for fractures and 0.886 for normal cases, the model effectively identifies true negatives. The overall accuracy of 92.14% highlights its reliability in correctly classifying cases by the area under the receiver operating characteristic curve.
We successfully used deep learning models for computer-assisted diagnosis of cervical spine fractures from radiographic x-ray images. This approach can assist the radiologist in screening, detecting, and diagnosing cervical spine fractures.
开发并评估一种利用卷积神经网络（CNN）从X线影像中对颈椎骨折进行计算机辅助诊断的技术。通过利用深度学习技术，该研究可能会改善患者预后并优化临床决策。
本研究从标准开源数据集存储库中获取了500张颈椎侧位X线影像，以使用CNN开发分类模型。所有影像均包含诊断信息，包括正常颈椎X线影像（n = 250）和颈椎骨折影像（n = 250）。该模型将对患者是否患有颈椎骨折进行分类。70%的影像为用于模型训练的训练数据集，30%用于测试。基于康斯坦茨信息挖掘器（KNIME）图形用户界面的编程实现了类别标签标注、数据预处理、CNN模型训练和性能评估。
用于检测颈椎骨折的模型在各项指标上均呈现出令人信服的结果。该模型对骨折的敏感性（召回率）值高达0.886，对正常病例的敏感性值为0.957，表明其在识别真阳性方面的能力。骨折的精确率值为0.954，正常病例的精确率值为0.893，突出了该模型将假阳性降至最低的能力。骨折的特异性值为0.957，正常病例的特异性值为0.886，表明该模型能有效识别真阴性。92.14%的总体准确率突出了其在通过受试者工作特征曲线下面积正确分类病例方面的可靠性。
我们成功地将深度学习模型用于从X线影像中对颈椎骨折进行计算机辅助诊断。这种方法可以协助放射科医生筛查、检测和诊断颈椎骨折。
