电子阿基米德螺旋神经网络 (ELANN)
James M Holcomb1, Joseph A Maldjian2, Yin Xi1
1Focused Ultrasound Lab and Program, Department of Radiology, UTSW Medical Center, Dallas, TX, USA; Advanced Neuroscience Imaging Research Lab, Department of Radiology, UTSW Medical Center, Dallas, TX, USA.
Parkinsonism & related disorders
|September 8, 2023
概括
一个新的人工智能模型客观地评估阿基米德螺旋对于基本震诊断,匹配高级神经科医生的熟练程度. 这种工具提高了震评估的一致性,提高了诊断准确度.
科学领域:
- 神经学 神经学
- 人工智能的人工智能
- 医疗成像医学成像
背景情况:
- 阿基米德螺旋是基本震的诊断工具.
- 医生对螺旋评级的主观性可能会影响诊断一致性.
- 需要一个客观的,标准化的震评估方法存在.
研究的目的:
- 开发一个客观的标准模型,用卷积神经网络进行基本震评估.
- 为了评估模型的表现与经验丰富的神经病学家在评分阿基米德螺旋的表现.
主要方法:
- 一个卷积神经网络在1653年的阿基米德螺旋图像上受训,由一名高级神经学家评价.
- 模型的表现与两个高级神经病学家在测试数据集上的评分进行了比较.
- 通过使用科恩的加权卡帕 (Cohen's Weighted Kappa) 来量化评级者之间的可靠性.
主要成果:
- 人工智能模型与参考标准 (Kappa 0.89-0.96) 达成高度一致.
- 该模型的表现与两位高级运动障碍神经病学家 (Kappa 0.86-0.98) 的表现相当.
- 重叠的置信区间表明模型和专家评级者之间的熟练程度相似.
结论:
- 开发的卷积神经网络模型客观地评估了阿基米德螺旋,其能力与经验丰富的神经学家相似.
- 这种人工智能工具提供了一种标准化和可靠的方法来评估基本震.
- 该模型有可能提高基本震监测中的诊断一致性和准确性.
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