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一个基于成像的机器学习模型在诊断肝硬化水出血时的临床风险得分中表现出色

Yin Gao¹, Qian Yu¹, Xiaohuan Li²

¹Department of Radiology, Zhongda Hospital, School of Medicine, Southeast University, 87 Ding Jia Qiao Road, Nanjing, 210009, Jiangsu, China.

European radiology

|July 15, 2023

概括

一个新的肝 (LS) 机器学习模型准确地预测了急性水出血 (AVB) 的肝患者的临床结果. 这种人工智能工具超越了传统的分数,有助于个人化治疗的临床决策.

科学领域:

放射学放射学是一门学科.
人工智能的人工智能
肝病学肝病学是一种肝病学.

背景情况:

急性水出血 (AVB) 是肝硬化患者的一种严重并发症.
预测AVB的临床终点对于管理至关重要.

研究的目的:

开发和验证一种使用对比增强CT的机器学习模型,以预测AVB.Cirrhotic患者的临床终点.
将模型的表现与传统的临床分数进行比较.

主要方法:

对330名患有AVB的肝硬化患者进行了回顾性研究.
分析了对比度增强CT和临床数据.
一个深度学习模型细分了肝脏和脏; 放射学特征被提取.
肝 (LS) 模型是在内部和外部测试集上构建和验证的.

主要成果:

LS模型表现出强大的诊断性能,AUC为0.782 (内部) 和0.789 (外部).
该模型在预测复合临床终点方面表现出高灵敏度 (≥80%).
在这两组测试中,LS模型的表现都超过了传统的临床评分.

结论:

肝机器学习模型是预测AVB的肝硬化患者的预后的一个有希望的工具.
这种人工智能驱动的方法可以改善临床决策和个性化治疗策略.
与现有的临床评分相比,LS模型提供了优越的预测能力.

关键词:

深度学习是一种深度学习.食道和胃的变形体.胃肠道出血是什么意思肝硬化是肝硬化的一种疾病.机器学习机器学习

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