基于深度学习的MRI全自动评估颈椎管狭窄的可行性。

IF 2.3 2区 医学 Q2 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING
Quantitative Imaging in Medicine and Surgery Pub Date : 2025-09-01 Epub Date: 2025-08-19 DOI:10.21037/qims-2025-67
Xiaochen Feng, Yaying Zhang, Minming Lu, Chao Ma, Xiaoqiang Miao, Jiacheng Yang, Lina Lin, Yueyi Zhang, Kai Zhang, Ning Zhang, Yan Kang, Yu Luo, Kai Cao
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引用次数: 0

摘要

背景:目前,还没有完全自动化的工具可用于评估颈椎管狭窄程度。本研究的目的是开发和验证人工智能(AI)算法在颈椎管狭窄评估中的应用。方法:在这项回顾性多中心研究中,纳入了2020年7月至2023年6月期间获得的颈椎磁共振成像(MRI)扫描。排除了脊柱内固定患者或图像质量不理想的研究。矢状面t2加权图像。上海第四人民医院(Hos. 1)和上海长征医院(Hos. 2)的培训数据由两名肌肉骨骼(MSK)放射科医师按照Kang的系统作为标准参考进行注释。首先,训练卷积神经网络(CNN)检测感兴趣区域(ROI),并使用第二个Transformer进行分类。在Hos. 2的内部测试集和上海长海医院的外部测试集(Hos. 3)上对深度学习(DL)模型的性能进行了评估,并在六个读者之间进行了比较。计算了检测精度、间一致性、灵敏度(SEN)和特异性(SPE)等指标。结果:共分析795例患者(平均年龄±标准差,55±14岁;女性346例),其中589例属于训练组(75%)和验证组(25%),206例属于内部测试组,95例属于外部测试组。训练了4个不同临床应用场景的任务,其准确率(ACC)在0.8993 ~ 0.9532之间。当使用Kang系统评分≥2作为内测集诊断中枢性颈椎管狭窄的阈值时,该算法和6位读卡器在受试者工作特征曲线(auc)下的面积相似,均为0.936[95%置信区间(CI): 0.916-0.955], SEN为90.3%,SPE为93.8%;DL模型的AUC为0.931 (95% CI: 0.917-0.946),外部测试集的SEN为100%,SPE为86.3%。对比DL方法、6台读卡器与参考标准间的MRI报告,相关分析显示相关性中等,R值为0.589 ~ 0.668。DL模型产生的升级(9.2%)和降级(5.1%)与六名读者大致相同。结论:DL模型可以完全自动、可靠地利用MRI扫描评估颈椎管狭窄。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Feasibility of fully automatic assessment of cervical canal stenosis using MRI via deep learning.

Feasibility of fully automatic assessment of cervical canal stenosis using MRI via deep learning.

Feasibility of fully automatic assessment of cervical canal stenosis using MRI via deep learning.

Feasibility of fully automatic assessment of cervical canal stenosis using MRI via deep learning.

Background: Currently, there is no fully automated tool available for evaluating the degree of cervical spinal stenosis. The aim of this study was to develop and validate the use of artificial intelligence (AI) algorithms for the assessment of cervical spinal stenosis.

Methods: In this retrospective multi-center study, cervical spine magnetic resonance imaging (MRI) scans obtained from July 2020 to June 2023 were included. Studies of patients with spinal instrumentation or studies with suboptimal image quality were excluded. Sagittal T2-weighted images were used. The training data from the Fourth People's Hospital of Shanghai (Hos. 1) and Shanghai Changzheng Hospital (Hos. 2) were annotated by two musculoskeletal (MSK) radiologists following Kang's system as the standard reference. First, a convolutional neural network (CNN) was trained to detect the region of interest (ROI), with a second Transformer for classification. The performance of the deep learning (DL) model was assessed on an internal test set from Hos. 2 and an external test set from Shanghai Changhai Hospital (Hos. 3), and compared among six readers. Metrics such as detection precision, interrater agreement, sensitivity (SEN), and specificity (SPE) were calculated.

Results: Overall, 795 patients were analyzed (mean age ± standard deviation, 55±14 years; 346 female), with 589 in the training (75%) and validation (25%) sets, 206 in the internal test set, and 95 in the external test set. Four tasks with different clinical application scenarios were trained, and their accuracy (ACC) ranged from 0.8993 to 0.9532. When using a Kang system score of ≥2 as a threshold for diagnosing central cervical canal stenosis in the internal test set, both the algorithm and six readers achieved similar areas under the receiver operating characteristic curve (AUCs) of 0.936 [95% confidence interval (CI): 0.916-0.955], with a SEN of 90.3% and SPE of 93.8%; the AUC of the DL model was 0.931 (95% CI: 0.917-0.946), with a SEN in the external test set of 100%, and a SPE of 86.3%. Correlation analysis comparing the DL method, the six readers, and MRI reports between the reference standard showed a moderate correlation, with R values ranging from 0.589 to 0.668. The DL model produced approximately the same upgrades (9.2%) and downgrades (5.1%) as the six readers.

Conclusions: The DL model could fully automatically and reliably assess cervical canal stenosis using MRI scans.

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来源期刊
Quantitative Imaging in Medicine and Surgery
Quantitative Imaging in Medicine and Surgery Medicine-Radiology, Nuclear Medicine and Imaging
CiteScore
4.20
自引率
17.90%
发文量
252
期刊介绍: Information not localized
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