AI模型统一处理胎心信号重建、分析与变异性评估，提升监测准确性。
Artificial Intelligence-Assistant Cardiotocography: Unified Model for Signal Reconstruction, Fetal Heart Rate Analysis, and Variability Assessment
- 基于大规模数据预训练+专家标注微调，实现噪声抑制与信号重建。
- 对危急减速和加速的检测敏感度达89.13%和特异度达92.04%。
- 符合临床标准，适用于产科胎心监护智能化升级场景。
胎儿心率（FHR）监测及其变异性评估对预防胎儿窘迫和不良结局至关重要。传统方法受限于设备性能、数据传输及医生主观判断。我们开发了专用于FHR监测的AI模型FHrCTG，有效抑制噪声并精准重构信号。模型在包含558,412个无标签数据点的大规模数据集上预训练，并利用7,266个专家审核样本进一步优化。为验证FHR，引入交叠标签交集（IOL）方法，将速率分析转化为分类判断。测试表明，该模型在识别危急减速（敏感度89.13%，特异度87.78%）和加速（敏感度62.5%，特异度92.04%）方面表现优异。基于Fischer临床标准，模型在验证周期性（AUC 0.7214）和振幅变化（AUC 0.9643）方面均取得良好效果。
原文摘要 · Abstract (English)
The monitoring of fetal heart rate (FHR) and the assessment of its variability are crucial for preventing fetal compromise and adverse outcomes. However, traditional methods encounter limitations arising from equipment performance, data transmission, and subjective assessments by doctors. We have developed a tailored AI-based FHrCTG model specifically for FHR monitoring, which effectively mitigates noise interference and precisely reconstructs signals. Our model was pre-trained on a massive dataset consisting of 558,412 unlabeled data points and further refined using 7,266 expert-reviewed entries. To validate FHR, we introduced the Intersection Overlapping Labels (IOL) approach, which transforms rate analysis into categorical judgments. Testing revealed that our model demonstrates high sensitivity and specificity in detecting critical FHR decelerations (89.13% and 87.78%, respectively) and accelerations (62.5% and 92.04%, respectively). Furthermore, based on Fischer's criteria for clinical application, our model achieved impressive AUC scores of 0.7214 and 0.9643 for verifying FHR periodicity and amplitude variation, respectively.
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