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基于CT的人工智能预测模型用于甲状腺眼睛疾病的眼动性得分

Zijia Liu¹, Kexin Tan², Haiyang Zhang²

¹Institute of Image Communication and Network Engineering, Shanghai Jiao Tong University, Shanghai, 200040, China.

|July 24, 2024

概括

使用轨道CT扫描的人工智能 (AI) 模型准确地在甲状腺眼病 (TED) 患者中得分眼动力. 这种人工智能方法提高了临床使用的评估效率和准确性.

科学领域:

眼科医生眼科眼科
医疗成像医学成像
人工智能的人工智能

背景情况:

甲状腺眼病 (TED) 是一种常见的轨道疾病.
眼部运动限制是患者的主要投诉,这给评估带来了挑战.

研究的目的:

用轨道计算机断层扫描 (CT) 图像开发人工智能 (AI) 模型,用于TED中的眼动力评分.
为了提高眼动力评估的效率和准确性.

主要方法:

在410组CT图像和临床数据上使用深度学习构建了三重分类预测模型.
用多种深度学习模型 (ResNet-34,Alex-Net,VGG16-Net) 来进行特征提取.
根据临床特征进行了子组分析,以测试模型的疗效.

主要成果:

在预测眼动力得分方面,ResNet-34网络实现了最高准确度 (0.907).
模型的准确性显示,活跃/不活跃的TED阶段或不同类型的双眼视之间没有显著差异.
人工智能模型在女性患者中比男性更准确 (p=0.02).

结论:

一个利用CT图像和临床数据的AI模型成功地自动化了TED患者的眼动性得分.
这种人工智能驱动的方法有可能提高临床实践的效率和准确性.
这些发现促进了人工智能在管理TED方面的更广泛的临床应用.

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.深度学习是一种深度学习.眼睛的运动性眼睛的运动性甲状腺眼睛疾病甲状腺眼病

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