The Current State of Artificial Intelligence on Detecting Pulmonary Embolism via Computerised Tomography Pulmonary Angiogram: A Systematic Review.
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Authors
Hassan, Moustafa Said Taman Ahmed
Elhotiby, Mahmoud Abdrabo Mahmoud
Shah, Viraj
Rocha, Henry
Rad, Arian Arjomandi
Miller, George
Malawana, Johann
Issue Date
2025-06-05
Type
Journal Article
Systematic Review
Systematic Review
Language
en
Keywords
CT pulmonary angiogram scan , artificial intelligence , data science , machine learning , medical technology , natural language processing , pulmonary embolism
Alternative Title
Abstract
Pulmonary embolism (PE) is a life-threatening condition with significant diagnostic challenges due to high rates of missed or delayed detection. Computed tomography pulmonary angiography (CTPA) is the current standard for diagnosing PE, however, demand for imaging places strain on healthcare systems and increases error rates. This systematic review aims to assess the diagnostic accuracy and clinical applicability of artificial intelligence (AI)-based models for PE detection on CTPA, exploring their potential to enhance diagnostic reliability and efficiency across clinical settings. A systematic review was conducted in accordance with Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Excerpta Medica Database (EMBASE), Medical Literature Analysis and Retrieval System Online (MEDLINE), Cochrane, PubMed, and Google Scholar were searched for original articles from inception to September 2024. Articles were included if they reported successful AI integration, whether partial or full, alongside CTPA scans for PE detection in patients. The literature search identified 919 articles, with 745 remaining after duplicate removal. Following rigorous screening and appraisal aligned with inclusion and exclusion criteria, 12 studies were included in the final analysis. A total of three primary AI modalities emerged: convolutional neural networks (CNNs), segmentation models, and natural language processing (NLP), collectively used in the analysis of 341,112 radiographic images. CNNs were the most frequently applied modality in this review. Models such as AdaBoost and EmbNet have demonstrated high sensitivity, with EmbNet achieving 88-90.9% per scan and reducing false positives to 0.45 per scan. AI shows significant promise as a diagnostic tool for identifying PE on CTPA scans, particularly when combined with other forms of clinical data. However, challenges remain, including ensuring generalisability, addressing potential bias, and conducting rigorous external validation. Variability in study methodologies and the lack of standardised reporting of key metrics complicate comparisons. Future research must focus on refining models, improving peripheral emboli detection, and validating performance across diverse settings to realise AI's potential fully.
Description
Copyright: © 2025 The Author(s).
https://creativecommons.org/licenses/by-nc/4.0/
Citation
Hassan, MSTA. et al. (2025) 'The Current State of Artificial Intelligence on Detecting Pulmonary Embolism via Computerised Tomography Pulmonary Angiogram: A Systematic Review', British Journal of Hospital Medicine. 86(6) pp. 1-21. Available at: https://doi.org/10.12968/hmed.2024.0757
Publisher
MA Healthcare
License
Journal
British journal of hospital medicine (London, England : 2005)
Volume
86
Issue
6
PubMed ID
ISSN
1750-8460