Journal Article

Artificial intelligence to improve the detection and risk stratification of acute pulmonary embolism (AID-PE): protocol for a pragmatic quasi-experimental comparator study

Gunning, Samuel George Sinclair, Myring, Gareth, Mackenzie Ross, Robert, Cookson, Andrew, Rodrigues, Jonathan Carl Luis, Lyen, Stephen, Seatter, Annette, Bartlett, Jonathan W, Mitchell, Paul, Stimpson, Darryl, Suntharalingam, Jay, Charters, Pia Frances Pemberton, Rossdale, Jennifer, Page, Joseph, Austin, Lisa, Hudson, Benjamin, McLeod, Hugh

2026

Overview

IntroductionPulmonary embolism (PE) is a potentially fatal condition requiring timely diagnosis and treatment. CT pulmonary angiography (CTPA) is the gold standard for diagnosis and indicates PE severity through radiological markers of right heart strain. However, accurate interpretation and communication of these findings is often suboptimal in real-world practice. Artificial intelligence (AI) could alleviate pressure on radiology services by supporting PE identification, risk stratification and worklist prioritisation. Before widespread adoption, AI tools must be rigorously validated for diagnostic accuracy, safety and clinical impact.Methods and analysisThis pragmatic single-centre, non-randomised quasi-experimental study will evaluate the diagnostic accuracy, feasibility, and clinical-cost impact of AI-assisted PE detection and risk stratification using AIDOC and IMBIO software. We will recruit two consecutive cohorts of adult patients undergoing CTPAs for suspected PE: a comparator cohort (12 months pre-AI implementation) and an intervention cohort (12 months post-AI implementation). AI will be applied retrospectively to the comparator cohort, while in the intervention cohort, radiologists will have contemporaneous access to the AI’s interpretation of CTPA images.A subset of retrospective scans, both PE-positive and PE-negative, will undergo expert thoracic radiologist review to establish a reference standard. Data on patient demographics, clinical management and outcomes will be collected. Clinical management pathways and patient outcomes will be compared between cohorts to assess AI’s influence on acute PE management. Health economic modelling will assess the cost-effectiveness of integrating AI technology within the diagnostic workflow of acute PE.Ethics and disseminationThis study was approved by the UK Healthcare Research authority (IRAS 311735, 10 May 2023). Ethical approval was granted by West of Scotland Research Ethics Service (23/WS/0067, 3 May 2023). Results will be shared with stakeholders, presented at national and international conferences, and published in open-access peer-reviewed journals.Trial registration numberNCT06093217.

• Publisher: British Medical Journal Publishing Group,BMJ Publishing Group LTD,BMJ Publishing Group
• Subject: Accuracy / Acute Disease / Algorithms / Artificial Intelligence / Cohort analysis / Computed tomography / Computed Tomography Angiography - methods / Decision making / Economic impact / Feasibility / Health Care Costs / Humans / Intervention / Mortality / Patients / Protocol / Pulmonary Disease / Pulmonary Embolism - diagnosis / Pulmonary Embolism - diagnostic imaging / Pulmonary embolisms / Radiology / Research Design / Respiratory Medicine / Retrospective Studies / Risk Assessment - methods / Thromboembolism