人工智能模型实时产前部透明度评估:高性能和工作流集成
Yuanji Zhang1,2,3, Xin Yang1,2,3, Chunya Ji4
1National-Regional Key Technology Engineering Laboratory for Medical Ultrasound, School of Biomedical Engineering, Health Science Center, Shenzhen University, No. 3688 Nanhai Avenue, Nanshan District, Shenzhen 518060, China.
Radiology. Artificial intelligence
|April 30, 2025
概括
人工智能模型准确地识别了部透光 (NT) 平面,并在产前超声波评估中测量NT厚度,显示与放射科医生的工作流程一致.
科学领域:
- 医疗成像医学成像
- 人工智能在医学中的应用
- 产前诊断 在产前诊断
背景情况:
- 额头半透明度 (NT) 测量对于早期查胎儿动脉化症至关重要.
- 在产前超声波 (US) 中精确高效的NT平面识别和测量对于可靠的查至关重要.
研究的目的:
- 开发和评估基于人工智能 (AI) 的模型,用于实时NT平面识别和测量.
- 与经验丰富的放射科医生相比,评估AI模型的准确性和一致性.
主要方法:
- 一项回顾性多中心研究开发并评估了自动识别和测量NT (AIM-NT) 模型.
- 在内部 (3959张图像) 和外部 (267张视频) 数据集上,AIM-NT模型的性能与放射科医生进行了比较.
- 分析了NT平面识别和厚度测量的差异.
主要成果:
- 在内部数据上,AIM-NT实现了NT平面识别的0.92的接收器操作特征曲线下的面积.
- 在NT平面识别准确度 (88.8%与87.6%) 或对外部数据的NT厚度测量方面,AIM-NT和放射科医生之间没有发现显著差异.
- 在NT平面识别时间和厚度测量中观察到高一致性 (平均差异为0.03毫米).
结论:
- AIM-NT模型在识别NT平面和在产前US上测量NT厚度方面表现出很高的准确性.
- 人工智能模型与放射科医生工作流程的差异很小,这表明它有可能被整合到临床实践中.
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