Automated Detection of Maternal Vascular Malperfusion Lesions of the Placenta using Machine Learning

Introduction Hypertensive disorders of pregnancy (HDP) and fetal growth restriction (FGR) are common obstetrical complications, often with pathological features of maternal vascular malperfusion (MVM) in the placenta. Currently, clinical placental pathology methods involve a manual visual examination of histology sections, a practice that can be resource-intensive and demonstrate moderate-to-poor inter-pathologist agreement on diagnostic outcomes, dependant on the degree of pathologist sub-specialty training. Methods This study aims to apply machine learning (ML) feature extraction methods to classify digital images of placental histopathology specimens, collected from cases of HDP [gestational hypertension (GH), preeclampsia (PE), PE + FGR], normotensive FGR, and healthy pregnancies, according to the presence or absence of MVM lesions. 159 digital images were captured from histological placental specimens, manually scored for MVM lesions (MVM- or MVM+) and used to develop a support vector machine (SVM) classifier model, using features extracted from pretrained ResNet18. The model was trained with and without data augmentation, and with and without data shuffling, and the performance of the classifiers assessed and compared through measurements of accuracy, precision, and recall using confusion matrices. Results The SVM model demonstrated accuracies between 7-78% for WSI-level MVM classification, with poorest performance observed on images with borderline MVM presence, as determined through post hoc observation. Conclusion The results are promising for the integration of ML methods into the placental histopathological examination process. Using this study as a proof-of-concept will lead our group and others to carry ML models further in placental histopathology.