Predicting abnormal fetal growth using deep learning

Kamil Mikolaj

doi:10.1038/s41746-025-01704-0

2025-05-29

Kamil Mikolaj

Anders Nymark Christensen

Caroline Taksøe‐Vester

Morten Bo Søndergaard Svendsen

Martin G. Tolsgaard *

DOI：10.1038/s41746-025-01704-0

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超声评估胎儿大小和生长是孕期监测胎儿健康状况的主要手段，因为孕周过小（SGA）或孕周过大（LGA）对胎儿和母亲均构成显著风险。本研究旨在提高对异常胎儿生长的预测准确性。我们开发了一种深度学习模型，该模型基于65,752名患者94,538次检查中提取的433,096张超声图像数据进行训练。与当前临床标准Hadlock公式相比，深度学习模型在检测SGA（58% vs 70%）和LGA（41% vs 55%）方面表现显著更优，p < 0.001。此外，与Hadlock公式相比，该模型的估计结果在所有人口统计学和技术变量方面均表现出显著更低的偏差。将皮质结构、肝脏纹理和皮肤厚度等关键解剖学特征纳入模型可能是导致预测准确率提升的原因。

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ULTRASOUND

WEIGHT

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MEDICINE

期刊

npj Digital Medicine

IF:

15.1

论文数:

3.3K

被引数:

1.5W

机构

Univ Copenhagen

学者数:

3.1K

论文数: 1.5K

被引数: 707

Tech Univ Denmark

学者数:

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论文数: 593

被引数: 297

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