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Paul M Murphy¹

¹University of California-San Diego, UCSD Radiology, 9500 Gilman Dr, La Jolla, 200 W Arbor Dr, San Diego, CA, 92103, USA. pmmurphy@health.ucsd.edu.

Journal of imaging informatics in medicine

|February 22, 2024

概括

一个新的神经网络可以通过分析胃肠道直径和位置来检测和描述CT扫描中的肠道阻塞. 这种人工智能工具对诊断这种急性腹痛的常见原因显示出希望.

科学领域:

医疗成像医学成像
人工智能在医学中的应用
胃肠病学胃肠病学

背景情况:

肠道阻塞是急性腹痛的常见原因,需要准确和及时的诊断.
目前的诊断方法依赖于解释成像发现,如肠道直径和位置.
需要先进的计算工具来提高诊断肠道阻塞的效率和准确性.

研究的目的:

开发和评估一个神经网络,用CT扫描来检测和表征肠道阻塞.
评估网络对胃肠道进行细分的能力,并预测直径和纵向位置等关键参数.
将神经网络的性能与手动测量和细分进行比较.

主要方法:

一项回顾性研究包括来自165名肠道阻塞患者的202个CT扫描.
一个多通道神经网络被训练用于胃肠道细分和预测直径和纵向位置,使用一种新的嵌入.
用Dice分数对细分和内部等级相关系数 (ICC) 进行直径和经度预测来评估性能.

主要成果:

神经网络在胃肠道细分方面获得了78%的Dice分数. 神经网络在胃肠道细分方面获得了78%的Dice分数.
对直径预测发现中等一致性 (ICC=0.72),对纵向位置预测发现良好一致性 (ICC=0.85).
该模型在检测扩大肠道 (AUROC=0.90) 和区分近距离/远距离胃肠道区域 (AUROC=0.95和0.90) 中表现出高精度.

结论:

开发的神经网络和嵌入显示了CT上肠道阻塞的自动检测和表征的潜力.
这种AI方法可以帮助临床医生通过提供肠道直径和位置的客观测量来诊断肠道阻塞.
进一步验证和整合到临床工作流程中可以提高因肠道阻塞而导致急性腹痛的患者的管理.

关键词:

肠道阻塞肠道阻塞嵌入式嵌入式嵌入式神经网络的神经网络的神经网络定量成像技术定量成像分段化分段化分段化分段化

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