ArticleEmergency radiology2025
Developing a novel deep learning-based model for automatic right ventricular parameters assessment on ctpa in pulmonary embolism.
Article in Emergency radiology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors.
Funding
Abstract
purposeTo develop an automated method for quantifying right ventricular parameters from CTPA in pulmonary embolism (PE).
methodIn this study, a U-Net based deep learning model was employed to perform automatic segmentation of the right ventricular, left ventricular, ascending aorta, and pulmonary arteries on CTPA images of patients with PE. The right ventricular to left ventricular diameter ratio (RV/LV), main pulmonary artery to ascending aorta diameter ratio (PA/AA), and ventricular septal angle (SA) were automatically calculated using a deep learning model based on anatomical structures. Additionally, two senior radiologists manually annotated the three sets of features to serve as the gold standard. Intra-class Correlation Coefficients (ICCs) were calculated to assess the inter-observer reliability of the proposed method by comparing the three sets of automatically derived measurements with the manual annotations.
resultsThe calculated ICCs between automatically derived and manually annotated results demonstrated overall high consistency across the three parameters: RV/LV: 0.76 (95% CI: 0.638–0.877); SA: 0.79 (95% CI: 0.602–0.897); PA/AA: 0.77 (95% CI: 0.672–0.885).
conclusionThis study developed a deep learning approach for automated and accurate quantification of right ventricular dysfunction in pulmonary embolism (PE), which was shown to be highly consistent with manually annotated results. This provides an automated approach for quantifying right ventricular parameters in PE.
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.