Wongthawat Liawrungrueang, Inbo Han, Watcharaporn Cholamjiak, Peem Sarasombath, K Daniel Riew
{"title":"利用卷积神经网络模型对颈椎骨折进行人工智能检测","authors":"Wongthawat Liawrungrueang, Inbo Han, Watcharaporn Cholamjiak, Peem Sarasombath, K Daniel Riew","doi":"10.14245/ns.2448580.290","DOIUrl":null,"url":null,"abstract":"<p><strong>Objective: </strong>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.</p><p><strong>Methods: </strong>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.</p><p><strong>Results: </strong>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.</p><p><strong>Conclusion: </strong>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.</p>","PeriodicalId":19269,"journal":{"name":"Neurospine","volume":null,"pages":null},"PeriodicalIF":3.8000,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11456954/pdf/","citationCount":"0","resultStr":"{\"title\":\"Artificial Intelligence Detection of Cervical Spine Fractures Using Convolutional Neural Network Models.\",\"authors\":\"Wongthawat Liawrungrueang, Inbo Han, Watcharaporn Cholamjiak, Peem Sarasombath, K Daniel Riew\",\"doi\":\"10.14245/ns.2448580.290\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Objective: </strong>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.</p><p><strong>Methods: </strong>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.</p><p><strong>Results: </strong>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.</p><p><strong>Conclusion: </strong>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.</p>\",\"PeriodicalId\":19269,\"journal\":{\"name\":\"Neurospine\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":3.8000,\"publicationDate\":\"2024-09-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11456954/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Neurospine\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.14245/ns.2448580.290\",\"RegionNum\":2,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"2024/9/30 0:00:00\",\"PubModel\":\"Epub\",\"JCR\":\"Q1\",\"JCRName\":\"CLINICAL NEUROLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Neurospine","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.14245/ns.2448580.290","RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2024/9/30 0:00:00","PubModel":"Epub","JCR":"Q1","JCRName":"CLINICAL NEUROLOGY","Score":null,"Total":0}
引用次数: 0
摘要
目的开发并评估一种使用卷积神经网络(CNN)的技术,该技术可通过 X 射线图像对颈椎骨折进行计算机辅助诊断。通过利用深度学习技术,该研究有可能改善患者的治疗效果和临床决策:本研究从标准开源数据集库中获取了 500 幅颈椎侧位 X 光图像,利用 CNN 开发了一个分类模型。所有图像都包含诊断信息,包括正常颈椎放射图像(n=250)和颈椎骨折图像(n=250)。该模型将对患者是否患有颈椎骨折进行分类。70%的图像是用于模型训练的训练数据集,30%用于测试。康斯坦茨信息挖掘器(KNIME)基于图形用户界面的编程实现了类标签注释、数据预处理、CNNs 模型训练和性能评估:对颈椎骨折检测模型的性能评估在各种指标上都取得了令人信服的结果。该模型对骨折的灵敏度(召回)值为 0.886,对正常病例的灵敏度(召回)值为 0.957,这表明该模型能够熟练识别真阳性病例。骨折的精确度值为 0.954,正常病例的精确度值为 0.893,凸显了该模型将误报率降至最低的能力。骨折的特异性值为 0.957,正常病例的特异性值为 0.886,该模型能有效识别真阴性病例。总体准确率为 92.14%,通过接收者工作特征曲线下的面积,凸显了该模型在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计算机辅助诊断X光影像中的颈椎骨折。这种方法可以帮助放射科医生筛查、检测和诊断颈椎骨折。
Artificial Intelligence Detection of Cervical Spine Fractures Using Convolutional Neural Network Models.
Objective: 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.
Methods: 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.
Results: 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.
Conclusion: 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.