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使用人工智能进行局部评估和小肠克朗氏病严重性评分

Binu E Enchakalody¹, Ashish P Wasnik², Mahmoud M Al-Hawary³

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

Academic radiology

|May 3, 2024

概括

一个新的AI模型使用CT肠道学 (CTE) 来自动化小肠中克罗恩氏病 (CD) 严重性得分. 这种方法提供了标准化,本地化的评估,改进了手工方法,以更好地管理疾病.

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:

目前克罗恩氏病 (CD) 严重程度评估依赖于手动测量和CT肠道图像 (CTE) 的定性评估,这些图像具有局限性.
小肠中复杂的解剖学和疾病分布对准确和标准化的评分提出了挑战.

研究的目的:

开发和验证一种机器学习和计算机视觉方法,用于局部化,自动化和标准化的小肠中CD严重程度的评分.
为了克服CTE成像中手动评估的局限性.

主要方法:

开发了一个混合模型,结合了深度学习,3D卷积神经网络 (CNN) 和随机森林.
两位放射科医生在236次CTE扫描远端和末端大肠骨中,以7.5毫米的间隔评估了疾病的严重程度.
模型的性能被评估使用精度,灵敏度,科恩得分和准确度在一个持有测试集.

主要成果:

混合型号在不同严重程度类别中实现了42.4%至84.1%的精度和灵敏度.
该模型的科恩得分 (κ = 0.83) 和准确度 (70.7%) 与放射科医生之间的观察者间一致性 (κ = 0.87,准确度 = 76.3%) 相似.
该模型准确地预测了疾病的长度 (r = 0.83),并以91.51%的准确度确定了中度至重度疾病部分.

结论:

自动化混合模型提供了一个标准化,可复制和定量化的小肠CD严重性局部评估.
这种人工智能驱动的方法在加强CD严重性评估和管理方面具有显著价值.

关键词:

检测CD严重性的检测CD严重性的检测克罗恩氏病是什么克罗恩氏病是什么疾病严重程度评分疾病严重程度评分在CD中的机器学习.小肠的人工智能

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