ReviewMethodist DeBakey cardiovascular journal2024
New Diagnostic Tools for Pulmonary Embolism Detection.
Review in Methodist DeBakey cardiovascular journal, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 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
6 citing papers in PubMed.
- 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
- Enhancing diagnostic safety with low iodine, low radiation CTPA classification using deep learning.Scientific reports · 2026Article
- Impact of an Artificial Intelligence-Guided Pulmonary Embolism Response Team (AIPERT) on Patient Transfers, Diagnosis, and Management: A Healthcare System Experience.Clinics and practice · 2025Article
- Advancements in Machine Learning and Artificial Intelligence in the Radiological Detection of Pulmonary Embolism.Cureus · 2025Review
- The promise and limitations of artificial intelligence in CTPA-based pulmonary embolism detection.Frontiers in medicine · 2025Review
- Artificial intelligence in modern clinical practice (Review).Medicine internationalReview
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The presentation of pulmonary embolism (PE) varies from asymptomatic to life-threatening, and management involves multiple specialists. Timely diagnosis of PE is based on clinical presentation, D-dimer testing, and computed tomography pulmonary angiogram (CTPA), and assessment by a Pulmonary Embolism Response Team (PERT) is critical to management. Artificial intelligence (AI) technology plays a key role in the PE workflow with automated detection and flagging of suspected PE in CTPA imaging. HIPAA-compliant communication features of mobile and web-based applications may facilitate PERT workflow with immediate access to imaging, team activation, and real-time information sharing and collaboration. In this review, we describe contemporary diagnostic tools, specifically AI, that are important in the triage and diagnosis of PE.
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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.