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机器学习方法在自动检测的CT肠道学在克罗恩病的发现:一个可行性研究

Ashish P Wasnik¹, Mahmoud M Al-Hawary², Binu Enchakalody³

¹Department of Radiology, University of Michigan, Ann Arbor, MI, USA; Department of Surgery, Morphomics Analysis Group, University of Michigan, Ann Arbor, MI, USA.

Clinical imaging

|July 4, 2024

概括

机器学习准确地检测和定位CT肠道图 (CTE) 上的克罗恩病发现,与放射科医生的性能相匹配. 这有助于提高对伊莱尔克罗恩病 (CD) 评估的标准化.

科学领域:

放射学放射学是一门学科.
人工智能的人工智能
胃肠病学胃肠病学

背景情况:

克罗恩病 (CD) 的定性发现很难标准化和量化.
CT肠道图 (CTE) 对于评估叶CD至关重要,但依赖于对定性发现的主观解释.

研究的目的:

为了评估机器学习 (ML) 以标准化检测叶CD的定性发现.
评估ML在CTE上确定这些发现的空间定位的能力.

主要方法:

两位腹部放射科医生对165个叶CDCTEs进行了回顾性审查.
开发随机森林 (RF) 和卷积神经网络 (CNN) 模型来预测定性发现.
使用AUC,灵敏度,特异性,精度和kappa协议进行性能评估.

主要成果:

放射科医生对局部化的一致性很好 (κ=0.66-0.73),除了脂肪浅 (κ=0.47).
射频模型取得了出色的性能 (AUC=0.91,灵敏度=0.81,特异性=0.85).
ML与放射科医生的模型协议与放射科医生之间的近似协议 (κ=0.67-0.76).

结论:

机器学习技术可以识别和定位CTE上的定性CD发现.
在分析中,ML的性能与经验丰富的放射科医生在分析CTE的ILEAALCD中相当.

关键词:

人工智能的人工智能是人工智能.计算机断层扫描 (CT) 是一种计算机断层扫描.克罗恩氏病克罗恩氏病是什么?腹腔镜学腹腔镜学腹腔镜学图像细分图像细分图像细分机器学习是机器学习.放射学计算机辅助诊断计算机辅助诊断

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