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深度学习用于对CT上的急性骨质疏松性脊椎骨折早期脊柱结核的歧视

Wenjun Liu¹, Jin Wang², Yiting Lei¹

¹Department of Orthopedics, First Affiliated Hospital, Chongqing Medical University, Chongqing, People's Republic of China.

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|January 8, 2025

概括

深度学习模型准确地区分脊柱结核病 (STB) 和骨质疏松性脊椎压缩骨折 (OVCF),优于脊柱外科医生,并提供了一个有前途的诊断工具.

科学领域:

脊柱成像分析脊柱成像分析
医学中的人工智能
在骨科中诊断的准确性

背景情况:

区分脊柱结核病 (STB) 和骨质疏松性脊椎压缩骨折 (OVCF) 对患者管理至关重要.
准确的早期诊断会影响治疗途径和患者的治疗结果.

研究的目的:

评估深度学习 (DL) 模型,以区分早期的STB与急性OVCF,并使用角CT图像.
提高诊断精度,减少对MRI和活检的依赖.

主要方法:

在373名患者的CT数据上训练并验证了DL模型 (MVITV2,Efficient-Net-B5,ResNet101,ResNet50).
将DL模型的性能与脊椎外科医生的盲目审查进行了比较.
评估模型使用准确度,精度,灵敏度,F1得分和AUC.

主要成果:

在内部验证中,MVITV2 DL模型实现了98.98%的准确性,100%的精度,97.97%的灵敏度和0.997 AUC.
DL模型显著优于脊椎外科医生 (77.38%-93.56%的准确率).
外部验证证实了模型的稳定性和通用性.

结论:

与人类专家相比,DL模型在区分STB和OVCF方面表现出更高的准确性.
人工智能驱动的诊断为脊柱疾病评估提供了一个非侵入性的,具有成本效益的替代方案.
这些发现凸显了人工智能的潜力,可以彻底改变脊柱诊断和改善患者护理.

关键词:

在CT成像中使用CT成像.深度学习是一种深度学习.诊断的准确性诊断的准确性骨质疏松性脊椎骨折骨质疏松性脊椎骨折脊柱结核病脊柱结核病脊柱结核

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