利用多模态深度学习进行胎盘增生谱检测
Placenta Accreta Spectrum Detection using Multimodal Deep Learning
AI总结：
本研究提出多模态深度学习方法，通过整合3D MRI和2D US数据，提高胎盘增生谱检测的准确率和AUC值，有效提升产前风险评估能力。
摘要：
胎盘增生谱（PAS）是一种危及生命的产科并发症，涉及异常的胎盘侵入子宫壁。早期和准确的产前诊断对于降低母体和新生儿的风险至关重要。本研究旨在开发并验证一种深度学习框架，通过整合多种成像模态来增强PAS的检测。设计了一种多模态深度学习模型，采用中间特征级融合架构，结合3D磁共振成像（MRI）和2D超声（US）扫描。经过系统比较分析后，选择了3D DenseNet121-Vision Transformer用于MRI，以及2D ResNet50用于US作为单模特征提取器。包含1,293例MRI和1,143例US扫描的curated数据集用于训练单模模型，配对的患者匹配MRI-US扫描样本被用于多模态模型的开发和评估。在独立的测试集上，多模态融合模型表现出优越的性能，准确率为92.5%，面积下接收者操作特征曲线（AUC）为0.927，优于MRI-only（82.5%，AUC 0.825）和US-only（87.5%，AUC 0.879）模型。整合MRI和US特征提供了互补的诊断信息，显示出增强产前风险评估和改善患者结局的强大力量。
英文摘要：
Placenta Accreta Spectrum (PAS) is a life-threatening obstetric complication involving abnormal placental invasion into the uterine wall. Early and accurate prenatal diagnosis is essential to reduce maternal and neonatal risks. This study aimed to develop and validate a deep learning framework that enhances PAS detection by integrating multiple imaging modalities. A multimodal deep learning model was designed using an intermediate feature-level fusion architecture combining 3D Magnetic Resonance Imaging (MRI) and 2D Ultrasound (US) scans. Unimodal feature extractors, a 3D DenseNet121-Vision Transformer for MRI and a 2D ResNet50 for US, were selected after systematic comparative analysis. Curated datasets comprising 1,293 MRI and 1,143 US scans were used to train the unimodal models and paired samples of patient-matched MRI-US scans was isolated for multimodal model development and evaluation. On an independent test set, the multimodal fusion model achieved superior performance, with an accuracy of 92.5% and an Area Under the Receiver Operating Characteristic Curve (AUC) of 0.927, outperforming the MRI-only (82.5%, AUC 0.825) and US-only (87.5%, AUC 0.879) models. Integrating MRI and US features provides complementary diagnostic information, demonstrating strong potential to enhance prenatal risk assessment and improve patient outcomes.
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