基于超声成像数据的深度学习和临床特征集成的预测模型,用于8个月以下儿童的静脉切割手术
Yu-Feng Qian1, Jin-Jin Zhou2, San-Li Shi3
1Radiology, Children's Hospital of Soochow University, Suzhou, Jiangsu, China.
BMJ open
|August 22, 2025
概括
一个结合深度学习 (DL) 和临床特征的新组合模型准确地预测了婴儿静脉切割的外科干预. 这种方法有望改善儿科患者的治疗策略.
科学领域:
- 儿童手术
- 医学成像分析
- 在医疗保健中的机器学习
背景情况:
- 在婴儿的手术紧急情况.
- 预测阴道减少后需要进行手术对患者的最佳结果至关重要.
- 目前的预测方法可能缺乏足够的准确性,需要先进的方法.
研究的目的:
- 为了确定阴囊减少失败的危险因素.
- 开发和验证一个结合深度学习 (DL) 和预测手术干预的临床特征的组合模型.
- 评估组合模型在8个月以下儿童的预测性能.
主要方法:
- 追溯和前性研究设计,涉及415例婴儿内.
- 使用预训练的DL网络提取深度转移学习功能.
- 使用后勤回归将DL特征与临床选特征集成.
- 结合模型与内部,外部和潜在队列中的独立临床和DL模型的验证.
主要成果:
- 与临床 (AUC: 0. 776- 0. 740) 和DL模型 (AUC: 0. 828- 0. 793) 相比,组合模型的预测性能更好 (AUC: 0. 911内部,0. 871,前性).
- 该模型显示了对手术干预需求的稳定和准确预测.
- 确定了导致阴道减少失败的关键风险因素.
结论:
- 结合DL和临床特征的组合模型为婴儿内肠切割手术提供了高稳定的预测准确性.
- 这种模式有可能显著提高临床决策和治疗策略.
- 进一步实施可能会改善儿科内宫受精的管理.
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