从共识到标准化:评估超声波成像中神经块细分的深度学习
Eric D Pelletier1, Sean D Jeffries1,2, Noam Suissa1
1From the Department of Experimental Surgery, McGill University Health Center, Montreal, Quebec, Canada.
A&A practice
|August 22, 2025
概括
深度学习模型有望在超声波图像中自动识别神经阻塞. 该R2U-Net模型表现出强的表现,有助于在程序中的专家评估.
科学领域:
- 医学成像
- 人工智能
- 麻醉学
背景情况:
- 深度学习 (DL) 在超声波图像中提供自动神经识别.
- 这项技术从专家标记的数据中学习来检测和突出神经.
- 精确的神经识别对于超声波导向的神经阻塞至关重要.
研究的目的:
- 评估深度学习模型在超声指导神经阻塞中识别神经的性能.
- 为了比较各种卷积神经网络架构对神经细分的有效性.
主要方法:
- 收集了9个神经阻断区域的3594张超声波图像.
- 每个区域的10张图片被用作测试,由10名麻醉师标记.
- 增加了3504张图像,以创建每个地区的25000张图像训练集,其中包括908张负面图像.
主要成果:
- R2U-Net模型获得了最高的平均子得分 (0.7619),优于其他模型.
- 经腹部平面 (TAP) 神经区域的性能有显著差异.
- 专家评价显示高准确度,在100%的脊柱神经预测中同意注射针.
结论:
- 深度学习模型,特别是R2U-Net,在超声波引导的程序中显示出一致的神经细分的潜力.
- 这些发现支持整合DL工具以提高神经阻断的准确性和安全性.
- 进一步验证和完善DL模型可以改善区域麻醉的临床应用.
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