ArticleThe American journal of pathology2020
Decidual Vasculopathy Identification in Whole Slide Images Using Multiresolution Hierarchical Convolutional Neural Networks.
Article in The American journal of pathology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
18 citing papers in PubMed, 33 citations in OpenAlex.
- Article
- Emerging modalities for neuroprognostication in neonatal encephalopathy: harnessing the potential of artificial intelligence.Pediatric research · 2025Review
- Article
- Artificial Intelligence in Placental Pathology: New Diagnostic Imaging Tools in Evolution and in Perspective.Journal of imaging · 2025Review
- Review
- Benchmarking pathology foundation models for non-neoplastic pathology in the placenta.medRxiv : the preprint server for health sciences · 2025Article
- Preeclampsia: Etiology, Pathophysiology, Risk Factors, Impact and Prevention: A Narrative Review.Iranian journal of public health · 2024Review
- Equipping computational pathology systems with artifact processing pipelines: a showcase for computation and performance trade-offs.BMC medical informatics and decision making · 2024Article
- Quantitative Modeling to Characterize Maternal Inflammatory Response of Histologic Chorioamnionitis in Placental Membranes.American journal of reproductive immunology (New York, N.Y. : 1989) · 2024Article
- Mapping cell-to-tissue graphs across human placenta histology whole slide images using deep learning with HAPPY.Nature communications · 2024Article
- Tissue Contamination Challenges the Credibility of Machine Learning Models in Real World Digital Pathology.Modern pathology : an official journal of the United States and Canadian Academy of Pathology, Inc · 2024Article
- Tissue contamination challenges the credibility of machine learning models in real world digital pathology.medRxiv : the preprint server for health sciences · 2023Article
- Imaging bridges pathology and radiology.Journal of pathology informatics · 2023Review
- Multiplexed high-throughput localized electroporation workflow with deep learning-based analysis for cell engineering.Science advances · 2022Article
- Project SWAVE 2.0: An overview of the study design for multimodal placental image acquisition and alignment.MethodsX · 2022Article
- High-Accuracy Oral Squamous Cell Carcinoma Auxiliary Diagnosis System Based on EfficientNet.Frontiers in oncology · 2022Article
- Machine Learning Based Prediction of Squamous Cell Carcinoma in Ex Vivo Confocal Laser Scanning Microscopy.Cancers · 2021Article
- GestAltNet: aggregation and attention to improve deep learning of gestational age from placental whole-slide images.Laboratory investigation; a journal of technical methods and pathology · 2021Article
Corrections and comments
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Authors and funding
7 authors at 3 institutions in 1 country.
Funding
Abstract
After a child is born, the examination of the placenta by a pathologist for abnormalities, such as infection or maternal vascular malperfusion, can provide important information about the immediate and long-term health of the infant. Detection of the pathologic placental blood vessel lesion decidual vasculopathy (DV) has been shown to predict adverse pregnancy outcomes, such as preeclampsia, which can lead to mother and neonatal morbidity in subsequent pregnancies. However, because of the high volume of deliveries at large hospitals and limited resources, currently a large proportion of delivered placentas are discarded without inspection. Furthermore, the correct diagnosis of DV often requires the expertise of an experienced perinatal pathologist. We introduce a hierarchical machine learning approach for the automated detection and classification of DV lesions in digitized placenta slides, along with a method of coupling learned image features with patient metadata to predict the presence of DV. Ultimately, the approach will allow many more placentas to be screened in a more standardized manner, providing feedback about which cases would benefit most from more in-depth pathologic inspection. Such computer-assisted examination of human placentas will enable real-time adjustment to infant and maternal care and possible chemoprevention (eg, aspirin therapy) to prevent preeclampsia, a disease that affects 2% to 8% of pregnancies worldwide, in women identified to be at risk with future pregnancies.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.