Obstetrics and Gynecology Hospital of Fudan University, Shanghai, China.
Department of Electronic Engineering, Fudan University, Shanghai, China.
J Clin Ultrasound. 2022 Sep;50(7):976-983. doi: 10.1002/jcu.23233. Epub 2022 Jul 10.
The goal of this study was to introduce PFCnet (placental features classification network), an multimodel model for evaluating and classifying placental features in gestational diabetes mellitus (GDM) and normal late pregnancy. Deep learning algorithms could be utilized to fully automate the examination of alterations in the placenta caused by hyperglycemia.
A total of 718 placental ultrasound images, including 139 cases of GDM, were collected, including gray-scale images (GSIs) and microflow images (MFIs). Ultrasonic assessment parameters and perinatal features were recorded. We divided gestational age into two categories for analysis (37 weeks and 37 weeks) based on the cut-off value level of placental maturity. The PFCnet model was introduced for identifying placental characteristics from normal and GDM pregnancies after extensive training and optimization. The model was scored using metrics such as sensitivity, specificity, accuracy, and the area under the curve (AUC).
In view of multimodal fusion (GSIs and MFIs) and deep network optimization training, the overall diagnostic performance of the PFCnet model depending on the region of interest (ROI) was excellent (AUC: 93%), with a sensitivity of 89%, a specificity of 92%, and an accuracy of 92% in the independent test set. The fusion features of GSIs and MFIs in the placenta showed a higher discriminative power than single-mode features (accuracy: Fusion 92% vs. GSIs 84% vs. MFIs 82%). The independent test set at 37 weeks exhibited a better specificity (75% vs. 69%) but a lower sensitivity(95% vs. 100%).
With its dual channel identification of placental parenchymal and vascular lesions in obstetric complications, the PFCnet classification model has the potential to be a useful tool for detecting placental tissue abnormalities caused by hyperglycemia.
本研究旨在介绍 PFCnet（胎盘特征分类网络），这是一种用于评估和分类妊娠期糖尿病（GDM）和正常晚期妊娠胎盘特征的多模型模型。深度学习算法可用于全自动检查由高血糖引起的胎盘变化。
共收集了 718 张胎盘超声图像，包括 139 例 GDM 病例，包括灰度图像（GSI）和微血流图像（MFI）。记录了超声评估参数和围产期特征。我们根据胎盘成熟度的截止值水平将胎龄分为两个类别进行分析（37 周和 37 周）。在广泛的培训和优化后，引入 PFCnet 模型从正常和 GDM 妊娠中识别胎盘特征。使用灵敏度、特异性、准确性和曲线下面积（AUC）等指标对模型进行评分。
鉴于多模态融合（GSI 和 MFI）和深度网络优化训练，PFCnet 模型的总体诊断性能（AUC：93%）非常出色，在独立测试集中的灵敏度为 89%，特异性为 92%，准确性为 92%。GSI 和 MFI 在胎盘中的融合特征比单模态特征具有更高的区分能力（准确性：融合 92%比 GSI 84%比 MFI 82%）。在独立的 37 周测试集中，特异性（75%比 69%）更好，但灵敏度较低（95%比 100%）。
PFCnet 分类模型具有双通道识别产科并发症中胎盘实质和血管病变的能力，有可能成为检测由高血糖引起的胎盘组织异常的有用工具。
