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开发和验证基于脑膜超声波成像的深度学习模型,用于周心室内心室出血检测和分级:一项两中心研究

Yahui Peng¹, Zhensheng Hu², Mianmian Wen³

¹School of Biomedical Engineering, Sun Yat-sen University, Shenzhen, China.

Pediatric radiology

|July 29, 2025

概括

一种新型的深度学习模型使用头骨超声波准确地检测和评分婴儿的周周内出血 (IVH). 这种由人工智能驱动的工具显示出改善新生儿护理的巨大潜力,特别是在社区医院.

科学领域:

新生儿神经学新生儿神经学
医学成像分析分析医学成像分析
医疗保健中的人工智能

背景情况:

周腹腔内出血 (IVH) 是新生儿关键性脑内出血,特别威胁早产婴儿.
膜超声波对于查新生儿IVH是必不可少的.
将人工智能与新生儿超声波集成,可以提高诊断准确性和效率.

研究的目的:

调查深度学习模型在从婴儿头骨超声图像中检测和分级周周直肠IVH时的有效性.
在多中心环境中评估AI模型的诊断性能.

主要方法:

开发"周心室IVH网"深度学习模型,使用卷积块注意力模块.
773例婴儿病例的回顾性分析,用于模型培训和验证.
未来的两中心验证使用287例婴儿病例.
使用包括AUC,精度,回忆和F1分数在内的指标进行评估.

主要成果:

该模型在回顾性数据 (AUC 0.99,F1分数 0.93) 上取得了高性能.
前性验证显示出强的表现 (AUC 0.961,精度 0.89).
意义地图可视化了影响AI检测的区域.

结论:

周周直肠IVH净模型显示了在儿科临床应用的巨大潜力.
人工智能与儿科超声波相结合,可以显著提高IVH诊断的准确性和效率.
该模型对初级和社区医院尤其有希望.

关键词:

大脑出血脑出血深度学习是一种深度学习.脑回声学 (Echoencephalography) 是一种脑回声学 (Echoencephalography) 的方法,可以通过脑回声学 (Echoencephalography) 来进行.婴儿婴儿婴儿婴儿婴儿婴儿超声波超声波是指超声波的使用.

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