一个人工智能模型的回顾性发展,将超声波和临床数据结合起来,用于儿童尾炎的分化
Rongying Tan1, Yongteng Li1, Jianjun Wang1
1Department of Pediatric Surgery, Zhujiang Hospital, Southern Medical University, Guangzhou 510282, Guangdong, China.
Emergency medicine international
|November 27, 2025
概括
使用超声波图像和临床数据的AI模型准确地区分了儿童复杂尾炎和不复杂的尾炎. 这种诊断工具的性能与经验丰富的外科医生相美.
科学领域:
- 儿科放射学 儿科放射学
- 人工智能在医学中的应用
- 医学成像分析 医学成像分析
背景情况:
- 在儿童中,区分复杂尾炎 (CA) 和非复杂尾炎 (UA) 对于适当的治疗至关重要.
- 当前的诊断方法可能具有挑战性,需要改进的工具来准确区分.
研究的目的:
- 开发和验证人工智能 (AI) 模型,以区分儿科复杂尾炎 (CA) 和无复杂尾炎 (UA).
- 整合多模式数据,包括超声波图像和临床信息,以提高诊断准确度.
主要方法:
- 对372例儿科尾炎病例进行了回顾性分析,并进行了手术前腹部超声波.
- 从超声波图像中提取深度学习 (DL) 和放射性特征,并与临床数据 (WBC,NEU,CRP) 结合.
- 四个模型 (Rad,DL,DTL,Combine) 被构建并使用ROC曲线,DCA进行评估,并与外科医生的表现进行比较.
主要成果:
- 结合AI模型,整合放射性,DL和临床特征,表现出高性能 (AUC:在训练中为0.940).
- 该模型的准确性 (0.862) 和正预测值 (0.896) 与高级外科医生相比,优于初级外科医生.
- 决策曲线分析证实了组合模型的临床实用性.
结论:
- 一个基于超声波的AI模型可靠地在儿科患者中区分CA和UA.
- 这种人工智能模型作为一种有价值的诊断支持工具,用于尾炎管理中的临床决策.
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