卷积神经网络算法的外部验证,用于在常规CT扫描中机会地检测脊椎骨折
Joeri Nicolaes1,2, Yandong Liu3, Yue Zhao4
1Department of Electrical Engineering (ESAT), Center for Processing Speech and Images, KU Leuven, Leuven, Belgium. joeri.nicolaes@kuleuven.be.
概括
一个卷积神经网络 (CNN) 算法在CT扫描中准确地识别了脊椎骨折 (VFs). 这种人工智能工具有望帮助放射科医生在常规腹部和胸部成像中早期检测VF.
科学领域:
- 放射学 放射学是一门学科.
- 人工智能的人工智能
- 医疗成像医学成像
背景情况:
- 脊椎骨折 (VFs) 是一种常见的骨脆弱性,经常被诊断不足.
- 早期识别VF对于及时干预和管理至关重要.
- 计算机断层扫描 (CT) 扫描被广泛用于医学成像,包括检测VFs.
研究的目的:
- 评估卷积神经网络 (CNN) 模型的诊断性能,用于自动检测脊椎骨折 (VFs).
- 用CT扫描评估CNN模型在外部验证队列中的有效性.
- 为了确定模型的准确性,灵敏度和特异性在识别VF时.
主要方法:
- 从4810名中国患者 (≥50岁) 的CT扫描 (胸部,腹部,胸脊柱) 的回顾性选择.
- 通过盲目阅读器使用半定量Genant分类来评估VF.
- 使用AUROC,准确性,卡帕,灵敏度和特异性评估CNN模型的性能.
主要成果:
- 在CNN模型中,AUROC为0.94,用于识别中度至重度VF的扫描.
- 该算法显示了高精度 (93%),灵敏度 (94%) 和特异性 (93%).
- 性能指标表明CNN模型具有强大的检测能力.
结论:
- 开发的CNN算法在CT扫描中检测VF方面表现出色.
- 人工智能工具有可能支持放射科医生在早期识别VF时.
- 外部验证证实了该算法在中国患者的常规临床实践中的实用性.
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