一个基于3D放射学的人工神经网络模型,用于MRI中的良性与恶性脊椎压缩骨折分类
Natália S Chiari-Correia1, Marcello H Nogueira-Barbosa2,3,4, Rodolfo Dias Chiari-Correia5
1Medical Artificial Intelligence Laboratory of the Ribeirão, Preto Medical School, University of São Paulo, 3900 Bandeirantes Avenue, Ribeirão Preto, SP, 14049-900, Brazil. natalia.chiari@alumni.usp.br.
Journal of digital imaging
|May 30, 2023
概括
使用3D放射性特征的人工神经网络模型在MRI上准确地区分良性和恶性脊椎压缩骨折 (VCF). 这种人工智能工具表现出色,有助于放射科医生进行VCF表征.
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:
- 脊椎压缩骨折 (VCFs) 需要准确区分良性和恶性原因.
- 区分良性和恶性VCF对于适当的患者管理至关重要.
- 目前的诊断方法在确定VCF的特征方面可能存在局限性.
研究的目的:
- 开发和验证一个人工神经网络 (ANN) 模型,以利用MRI的3D放射性特征来区分良性和恶性VCF.
- 在回顾性队列中评估ANN模型的诊断性能.
主要方法:
- 从91名患有VCFs的患者的斜T1加权的腰椎脊柱MRI的回顾性分析.
- 断裂的脊椎体的三维细分和放射性特征的提取.
- 训练和验证多层感知神经网络使用包装方法进行特征选择.
- 使用十倍交叉验证和独立测试集进行评估.
主要成果:
- 该ANN模型在区分良性和恶性VCF方面取得了出色的表现.
- 内部验证:ROC AUC为0.98,准确率为95%,灵敏度为93.5%,特异性为96.3%.
- 独立测试组验证:ROC AUC为0.97,准确率为93.3%,灵敏度为93.3%,特异性为93.3%.
结论:
- 拟议的ANN模型利用3D放射性特征,在区分良性与恶性VCF方面表现出高度准确性.
- 这种由人工智能驱动的方法显示出作为放射科医生在VCF表征中的辅助工具的重大前景.
- 这些发现表明,放射学和人工智能在改善VCF的诊断工作流程方面可能发挥作用.
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