ArticleRadiology. Artificial intelligence2021
Deep Learning for Pulmonary Embolism Detection: Tackling the RSNA 2020 AI Challenge.
Article in Radiology. Artificial intelligence, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 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.
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Who cites it
7 citing papers in PubMed, 17 citations in OpenAlex.
- External validation of a top-ranked model from the RSNA pulmonary embolism detection challenge: assessment of generalizability.Scientific reports · 2026Article
- Artificial intelligence for abdominopelvic trauma imaging: trends, gaps, and future directions.Abdominal radiology (New York) · 2025Review
- Comprehensive review of pulmonary embolism imaging: past, present and future innovations in computed tomography (CT) and other diagnostic techniques.Japanese journal of radiology · 2025Review
- AI-Enhanced Deep Learning Framework for Pulmonary Embolism Detection in CT Angiography.Bioengineering (Basel, Switzerland) · 2025Article
- Independent Evaluation of a Commercial AI Software for Incidental Findings of Pulmonary Embolism (IPE) on a Large Hospital Retrospective Dataset.Radiology research and practice · 2025Article
- Artificial intelligence-based pulmonary embolism classification: Development and validation using real-world data.PloS one · 2024Article
- The Stanford Medicine data science ecosystem for clinical and translational research.JAMIA open · 2023Article
Corrections and comments
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Authors and funding
1 author at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In 2020, the Radiological Society of North America and Society of Thoracic Radiology sponsored a machine learning competition to detect and classify pulmonary embolism (PE). This challenge was one of the largest of its kind, with more than 9000 CT pulmonary angiography examinations comprising almost 1.8 million expertly annotated images. More than 700 international teams competed to predict the presence of PE on individual axial images, the overall presence of PE in the CT examination (with chronicity and laterality), and the ratio of right ventricular size to left ventricular size. This article presents a detailed overview of the second-place solution. Source code and models are available at
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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.