The Role of Artificial Intelligence in Diagnosing Pulmonary Embolism: A Systematic Review and Meta-analysis
Archives of Academic Emergency Medicine,
Vol. 13 No. 1 (2025),
6 September 2025
,
Page e86
https://doi.org/10.22037/aaem.v13i1.2720
Abstract
Introduction: Missed or delayed diagnosis of pulmonary embolism (PE) is associated with increased morbidity, mortality, and longer hospitalizations. This study aimed to evaluate the diagnostic accuracy of Artificial Intelligence (AI) models in detecting PE across imaging.
Methods: We systematically searched PubMed/MEDLINE, Scopus, Embase and Web of Science from inception to 1 January 2025 without language or regional limits. After removing duplicate results, the remaining records were screened through titles/abstracts, and two reviewers independently assessed full texts. Risk of bias was evaluated in duplicate with the QUADAS-2 tool. Pooled sensitivity, specificity, positive and negative likelihood ratios, diagnostic odds ratio and area under the ROC curve were calculated with random-effects models in STATA 17. Heterogeneity was quantified with Cochran’s Q and I², while we explored its sources using subgroup analyses (for categorical moderators) and meta-regression (for continuous moderators). Publication bias was assessed with Deeks’ funnel plot and trim-and-fill, and we examined robustness through leave-one-out sensitivity analyses.
Results: A total of 1,432 records were identified through database searches, with 654 duplicates removed. After screening titles and abstracts of 787 articles, 256 full-text articles were assessed for eligibility, and 28 studies met the inclusion criteria. Internal validation phases included 43,330 participants (4,866 PE-positive, 38,463 PE-negative), while external validation phases comprised 3,588 participants (1,699 PE-positive, 1,889 PE-negative). In the internal validation phase, the pooled sensitivity and specificity of AI in PE diagnosis across imaging were 0.91 (95% confidence interval (CI): 0.88–0.95; I²=9%) and 0.94 (95% CI: 0.86–0.98; I²=99.78%), respectively. The positive likelihood ratio (PLR) was 16.08, and the negative likelihood ratio (NLR) was 0.09, both statistically significant (P < 0.001). The pooled diagnostic odds ratio (DOR) was 163.55 (95% CI: 71.30-375.14, I2: 96.1), and the area under the curve (AUC) was 0.95 (95% CI: 0.93 to 0.97), indicating excellent accuracy. In external validation, the pooled sensitivity and specificity were slightly lower at 0.89 (95% CI: 0.79–0.95; I²=95.60%) and 0.88 (95% CI: 0.80–0.93; I²=91.48%), respectively. The DOR was 59.65 (95% CI: 23.53 to 151.17, I2: 89.6) and AUC was 0.94 (95% CI: 0.92 to 0.96, I2: 89.6). There was no significant publication bias detected.
Conclusion: AI models achieved high diagnostic accuracy in detecting PE through imaging. However, this performance tends to decrease from internal to external validation, highlighting limitations in generalizability. Additionally, substantial heterogeneity was observed across studies, as indicated by high I² values, which should be considered when interpreting the pooled estimates.
- Pulmonary Embolism
- artificial intelligence
- Computed Tomography Angiography
- Systematic Review
- Meta-analysis
How to Cite
References
References:
1. Beckman MG, Hooper WC, Critchley SE, Ortel TL. Venous thromboembolism: a public health concern. American journal of preventive medicine. 2010;38(4):S495–S501.
2. Barnes GD, Muzikansky A, Cameron S, Giri J, Heresi GA, Jaber W, et al. Comparison of 4 acute pulmonary embolism mortality risk scores in patients evaluated by pulmonary embolism response teams. JAMA network open. 2020;3(8):e2010779–e.
3. Peris M, López-Nuñez JJ, Maestre A, Jimenez D, Muriel A, Bikdeli B, et al. Clinical characteristics and 3-month outcomes in cancer patients with incidental versus clinically suspected and confirmed pulmonary embolism. European Respiratory Journal. 2021;58(1).
4. Barco S, Valerio L, Ageno W, Cohen AT, Goldhaber SZ, Hunt BJ, et al. Age-sex specific pulmonary embolism-related mortality in the USA and Canada, 2000–18: an analysis of the WHO Mortality Database and of the CDC Multiple Cause of Death database. The Lancet Respiratory Medicine. 2021;9(1):33–42.
5. Lucassen WA, Beenen LF, Büller HR, Erkens PM, Schaefer-Prokop CM, van den Berk IA, et al. Concerns in using multi-detector computed tomography for diagnosing pulmonary embolism in daily practice. A cross-sectional analysis using expert opinion as reference standard. Thrombosis research. 2013;131(2):145–9.
6. Lim KY, Kligerman SJ, Lin CT, White CS. Missed pulmonary embolism on abdominal CT. American Journal of Roentgenology. 2014;202(4):738–43.
7. Kwok CS, Wong CW, Lovatt S, Myint PK, Loke YK. Misdiagnosis of pulmonary embolism and missed pulmonary embolism: A systematic review of the literature. Health Sciences Review. 2022;3:100022.
8. Yu H, Yang LT, Zhang Q, Armstrong D, Deen MJ. Convolutional neural networks for medical image analysis: state-of-the-art, comparisons, improvement and perspectives. Neurocomputing. 2021;444:92–110.
9. Kshatri SS, Singh D. Convolutional neural network in medical image analysis: a review. Archives of Computational Methods in Engineering. 2023;30(4):2793–810.
10. Wiklund P, Medson K, Elf J. Incidental pulmonary embolism in patients with cancer: prevalence, underdiagnosis and evaluation of an AI algorithm for automatic detection of pulmonary embolism. European Radiology. 2023;33(2):1185–93.
11. Stamate E, Piraianu A-I, Ciobotaru OR, Crassas R, Duca O, Fulga A, et al. Revolutionizing cardiology through artificial intelligence—Big data from proactive prevention to precise diagnostics and cutting-edge treatment—A comprehensive review of the past 5 years. Diagnostics. 2024;14(11):1103.
12. Mohanarajan M, Salunke PP, Arif A, Gonzalez PMI, Ospina D, Benavides DS, et al. Advancements in Machine Learning and Artificial Intelligence in the Radiological Detection of Pulmonary Embolism. Cureus. 2025;17(1).
13. Higgins JP, Thompson SG, Deeks JJ, Altman DG. Measuring inconsistency in meta-analyses. bmj. 2003;327(7414):557–60.
14. Cheikh AB, Gorincour G, Nivet H, May J, Seux M, Calame P, et al. How artificial intelligence improves radiological interpretation in suspected pulmonary embolism. European radiology. 2022;32(9):5831–42.
15. Teigen CL, Maus TP, Sheedy 2nd P, Stanson AW, Johnson CM, Breen JF, et al. Pulmonary embolism: diagnosis with contrast-enhanced electron-beam CT and comparison with pulmonary angiography. Radiology. 1995;194(2):313–9.
16. Xi L, Xu F, Kang H, Deng M, Xu W, Wang D, et al. Clot ratio, new clot burden score with deep learning, correlates with the risk stratification of patients with acute pulmonary embolism. Quantitative Imaging in Medicine and Surgery. 2024;14(1):86–97.
17. Liu W, Liu M, Guo X, Zhang P, Zhang L, Zhang R, et al. Evaluation of acute pulmonary embolism and clot burden on CTPA with deep learning. European radiology. 2020;30(6):3567–75.
18. Lanza E, Ammirabile A, Francone M. nnU-Net-based deep-learning for pulmonary embolism: detection, clot volume quantification, and severity correlation in the RSPECT dataset. European Journal of Radiology. 2024;177:111592.
19. Diaz-Lorenzo I, Alonso-Burgos A, Friera Reyes A, Pacios Blanco RE, de Benavides Bernaldo de Quiros MdC, Gallardo Madueño G. Current role of CT pulmonary angiography in pulmonary embolism: A state-of-the-art review. Journal of Imaging. 2024;10(12):323.
20. Li L, Peng M, Zou Y, Li Y, Qiao P. The promise and limitations of artificial intelligence in CTPA-based pulmonary embolism detection. Frontiers in Medicine. 2025;12:1514931.
21. Li Y, Zhang L, Liu H, Li Y, Liu Z. Research progress of artificial intelligence and machine learning in pulmonary embolism. Frontiers in Medicine. 2025;12:1577559.
22. Naser AM, Vyas R, Morgan AA, Kalaiger AM, Kharawala A, Nagraj S, et al. Role of Artificial Intelligence in the Diagnosis and Management of Pulmonary Embolism: A Comprehensive Review. Diagnostics. 2025;15(7):889.
23. Mudrik A, Efros O. Artificial Intelligence and Venous Thromboembolism: a narrative review of applications, benefits, and limitations. Acta Haematologica. 2025.
24. Huang S-C, Kothari T, Banerjee I, Chute C, Ball RL, Borus N, et al. PENet—a scalable deep-learning model for automated diagnosis of pulmonary embolism using volumetric CT imaging. NPJ digital medicine. 2020;3(1):61.
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