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. ### Competing Interest Statement The authors have declared no competing interest. ### Funding Statement This work was supported by the Canadian Institute of Health Research (CIHR; #CPG- 170604, awarded to SAB and ADCC; #PJT-153055, awarded to SAB) and the Natural Sciences and Engineering Research Council of Canada (NSERC; #CHRP-549538-20, awarded to SAB and ADCC). ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: The Research Ethics board of the University of Ottawa gave ethical approval for this work (#H-08-18-1023). The Research Ethics board of the Mount Sinai Hospital (Toronto) gave ethical approval for this work (#13-0212-E. I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes All data produced in the present work are contained in the manuscript.