ArticleNPJ digital medicine2020
PENet-a scalable deep-learning model for automated diagnosis of pulmonary embolism using volumetric CT imaging.
Article in NPJ digital medicine, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 78 papers, 3 of them syntheses that pooled it.
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
78 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Accuracy of AI-Based Algorithms in Pulmonary Embolism Detection on Computed Tomographic Pulmonary Angiography: An Updated Systematic Review and Meta-analysis.Journal of imaging informatics in medicine · 2026Pooled it
- Deep learning applications in visual data for benign and malignant hematologic conditions: a systematic review and visual glossary.Haematologica · 2023Pooled it
- Deep learning for pulmonary embolism detection on computed tomography pulmonary angiogram: a systematic review and meta-analysis.Scientific reports · 2021Pooled it
- Predictive and operational artificial intelligence in emergency radiology for early risk stratification in acute care settings: a scoping review.European radiology · 2026Article
- Artificial intelligence in computed tomography imaging for pulmonary embolism: a narrative review from computed tomography pulmonary angiography to non-contrast computed tomography.Quantitative imaging in medicine and surgery · 2026Review
- External validation of a top-ranked model from the RSNA pulmonary embolism detection challenge: assessment of generalizability.Scientific reports · 2026Article
- Development and validation of the IVENUS artificial intelligence model for automated CEAP classification of early-stage chronic venous disease using lower limb photographs.Journal of vascular surgery. Venous and lymphatic disorders · 2026Article
- Artificial intelligence-based algorithm for predicting outcomes in early-stage lung cancer: An annotation-free imaging artificial intelligence study.JTCVS open · 2026Article
- Commercial artificial intelligence-assisted performance and interpretation time of first-on-call radiology residents using computed tomography pulmonary angiography to detect pulmonary embolism: a multireader, multicenter study.Quantitative imaging in medicine and surgery · 2026Article
- Article
- A comprehensive review of explainable artificial intelligence in healthcare methods, evaluation, and clinical integration.iScience · 2026Review
- 3D region-growing nnU-Net improves pulmonary embolism detection on CTPA: a dual-cohort validation study.European radiology experimental · 2026Article
- AI-Based Pulmonary Embolism Detection: The Added Value of a False-Positive Reduction Module over a Region Proposal Network.Diagnostics (Basel, Switzerland) · 2026Article
- Enhancing diagnostic safety with low iodine, low radiation CTPA classification using deep learning.Scientific reports · 2026Article
- Deep learning-based segmentation of acute pulmonary embolism in cardiac CT images.International journal of computer assisted radiology and surgery · 2026Article
- Advancing Pulmonary Embolism Detection with Integrated Deep Learning Architectures.Journal of imaging informatics in medicine · 2026Article
- Revolutionizing non-traumatic acute care: a review of the role of artificial intelligence and machine learning in triaging and diagnosis.Acute and critical care · 2026Article
- A deep learning model for the interpretable identification of pulmonary thromboembolism from computed tomography pulmonary angiography.Frontiers in cardiovascular medicine · 2026Article
- HUCSR-Net: Automated Pulmonary Embolism Detection Using 3D Deep Learning.F1000Research · 2026Article
- Comprehensive review of pulmonary embolism imaging: past, present and future innovations in computed tomography (CT) and other diagnostic techniques.Japanese journal of radiology · 2025Review
18 more citing papers are in PubMed but not listed here.
Corrections and comments
- Erratum issued
Authors and funding
17 authors.
Funding
Abstract
Pulmonary embolism (PE) is a life-threatening clinical problem and computed tomography pulmonary angiography (CTPA) is the gold standard for diagnosis. Prompt diagnosis and immediate treatment are critical to avoid high morbidity and mortality rates, yet PE remains among the diagnoses most frequently missed or delayed. In this study, we developed a deep learning model-PENet, to automatically detect PE on volumetric CTPA scans as an end-to-end solution for this purpose. The PENet is a 77-layer 3D convolutional neural network (CNN) pretrained on the Kinetics-600 dataset and fine-tuned on a retrospective CTPA dataset collected from a single academic institution. The PENet model performance was evaluated in detecting PE on data from two different institutions: one as a hold-out dataset from the same institution as the training data and a second collected from an external institution to evaluate model generalizability to an unrelated population dataset. PENet achieved an AUROC of 0.84 [0.82-0.87] on detecting PE on the hold out internal test set and 0.85 [0.81-0.88] on external dataset. PENet also outperformed current state-of-the-art 3D CNN models. The results represent successful application of an end-to-end 3D CNN model for the complex task of PE diagnosis without requiring computationally intensive and time consuming preprocessing and demonstrates sustained performance on data from an external institution. Our model could be applied as a triage tool to automatically identify clinically important PEs allowing for prioritization for diagnostic radiology interpretation and improved care pathways via more efficient diagnosis.
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.