ArticleScientific reports2020
Deep Learning-Based Quantification of Pulmonary Hemosiderophages in Cytology Slides.
Article in Scientific reports, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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
21 citing papers in PubMed, 60 citations in OpenAlex.
- Artificial intelligence-enabled histological analysis in pre-clinical respiratory disease models: a scoping review.European respiratory review : an official journal of the European Respiratory Society · 2026Article
- Artificial Intelligence in Veterinary Clinical Pathology-An Introduction and Review.Veterinary clinical pathology · 2025Review
- A Performance Comparison of Different YOLOv7 Networks for High-Accuracy Cell Classification in Bronchoalveolar Lavage Fluid Utilising the Adam Optimiser and Label Smoothing.Journal of imaging informatics in medicine · 2025Article
- Comparison of artificial intelligence image processing with manual leucocyte differential to score immune cell infiltration in a mouse infection model of cystic fibrosis.Journal of pathology informatics · 2025Article
- Deep learning-based object detection algorithms in medical imaging: Systematic review.Heliyon · 2025Review
- A multicenter study to evaluate the analytical precision by pathologists using the Aperio GT 450 DX.Journal of pathology informatics · 2024Article
- Article
- Comparison of Sysmex XN-V body fluid mode and deep-learning-based quantification with manual techniques for total nucleated cell count and differential count for equine bronchoalveolar lavage samples.BMC veterinary research · 2024Article
- Automated diagnosis of 7 canine skin tumors using machine learning on H&E-stained whole slide images.Veterinary pathology · 2023Article
- A comprehensive multi-domain dataset for mitotic figure detection.Scientific data · 2023Article
- Cytologic scoring of equine exercise-induced pulmonary hemorrhage: Performance of human experts and a deep learning-based algorithm.Veterinary pathology · 2023Article
- Article
- Pan-tumor T-lymphocyte detection using deep neural networks: Recommendations for transfer learning in immunohistochemistry.Journal of pathology informatics · 2023Article
- Inter-species cell detection - datasets on pulmonary hemosiderophages in equine, human and feline specimens.Scientific data · 2022Article
- Computer-assisted mitotic count using a deep learning-based algorithm improves interobserver reproducibility and accuracy.Veterinary pathology · 2022Article
- Validation of a deep learning-based image analysis system to diagnose subclinical endometritis in dairy cows.PloS one · 2022Article
- Validation of digital microscopy: Review of validation methods and sources of bias.Veterinary pathology · 2022Review
- Classification for avian malaria parasite Plasmodium gallinaceum blood stages by using deep convolutional neural networks.Scientific reports · 2021Article
- EXACT: a collaboration toolset for algorithm-aided annotation of images with annotation version control.Scientific reports · 2021Article
- A completely annotated whole slide image dataset of canine breast cancer to aid human breast cancer research.Scientific data · 2020Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
16 authors at 5 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Exercise-induced pulmonary hemorrhage (EIPH) is a common condition in sport horses with negative impact on performance. Cytology of bronchoalveolar lavage fluid by use of a scoring system is considered the most sensitive diagnostic method. Macrophages are classified depending on the degree of cytoplasmic hemosiderin content. The current gold standard is manual grading, which is however monotonous and time-consuming. We evaluated state-of-the-art deep learning-based methods for single cell macrophage classification and compared them against the performance of nine cytology experts and evaluated inter- and intra-observer variability. Additionally, we evaluated object detection methods on a novel data set of 17 completely annotated cytology whole slide images (WSI) containing 78,047 hemosiderophages. Our deep learning-based approach reached a concordance of 0.85, partially exceeding human expert concordance (0.68 to 0.86, mean of 0.73, SD of 0.04). Intra-observer variability was high (0.68 to 0.88) and inter-observer concordance was moderate (Fleiss' kappa = 0.67). Our object detection approach has a mean average precision of 0.66 over the five classes from the whole slide gigapixel image and a computation time of below two minutes. To mitigate the high inter- and intra-rater variability, we propose our automated object detection pipeline, enabling accurate, reproducible and quick EIPH scoring in WSI.
Indexed as
Identifiers
What OpenQuestion 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 OpenQuestion graph.