ArticleNature communications2024
Mapping cell-to-tissue graphs across human placenta histology whole slide images using deep learning with HAPPY.
Article in Nature communications, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
19 citing papers in PubMed, 23 citations in OpenAlex.
- Reconstruction of Cell Diversity and Cell Lineages from Somatic Mutations in Single-Cell Transcriptomic Data.DNA research : an international journal for rapid publication of reports on genes and genomes · 2026Article
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- BRIDGING COTYLEDON PATHOLOGY AND PERFUSION IN HEALTHY PRIMATE PREGNANCY.bioRxiv : the preprint server for biology · 2026Article
- Automated detection of fetal vascular malperfusion via data augmentation and algorithm improvement.Scientific reports · 2026Article
- Multi-omics insights into immune tolerance at the maternal-fetal interface in recurrent pregnancy loss: mechanisms, integration challenges, and translational perspectives.Frontiers in immunology · 2026Review
- Biologically Inspired Digital Histology for Deep Phenotyping of Placental Composition Changes Across Major Lesion Types.bioRxiv : the preprint server for biology · 2025Article
- Emerging modalities for neuroprognostication in neonatal encephalopathy: harnessing the potential of artificial intelligence.Pediatric research · 2025Review
- Article
- Scalable Deep Learning of Histology Images Reveals Genetic and Phenotypic Determinants of Adipocyte Hypertrophy.medRxiv : the preprint server for health sciences · 2025Article
- 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
- Insights into the Global and Mexican Context of Placental-Derived Pregnancy Complications.Biomedicines · 2025Review
- Machine learning methods for histopathological image analysis: Updates in 2024.Computational and structural biotechnology journal · 2025Review
- Cross-modal contrastive learning for unified placenta analysis using photographs.Patterns (New York, N.Y.) · 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
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
15 authors at 9 institutions in 5 countries.
Funding
Abstract
Accurate placenta pathology assessment is essential for managing maternal and newborn health, but the placenta's heterogeneity and temporal variability pose challenges for histology analysis. To address this issue, we developed the 'Histology Analysis Pipeline.PY' (HAPPY), a deep learning hierarchical method for quantifying the variability of cells and micro-anatomical tissue structures across placenta histology whole slide images. HAPPY differs from patch-based features or segmentation approaches by following an interpretable biological hierarchy, representing cells and cellular communities within tissues at a single-cell resolution across whole slide images. We present a set of quantitative metrics from healthy term placentas as a baseline for future assessments of placenta health and we show how these metrics deviate in placentas with clinically significant placental infarction. HAPPY's cell and tissue predictions closely replicate those from independent clinical experts and placental biology literature.
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What Socratic holds
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 Socratic graph.