Artificial intelligence in computed tomography imaging for pulmonary embolism: a narrative review from computed tomography pulmonary angiography to non-contrast computed tomography
Abstract
Background and objective: Pulmonary embolism (PE) is a common life-threatening cardiovascular disorder. Computed tomography pulmonary angiography (CTPA) is the diagnostic reference standard, but iodinated contrast use may be limited by renal insufficiency, allergy, or other patient factors. Non-contrast computed tomography (NCCT) avoids contrast administration but has lower intrinsic diagnostic accuracy. This narrative review summarizes artificial intelligence (AI) applications for PE assessment across CTPA and NCCT, compares their clinical roles, and identifies evidence gaps for translation.
Methods: A structured literature search was conducted across PubMed, Google Scholar, Web of Science, and IEEE (Institute of Electrical and Electronics Engineers) Xplore from database inception to June 2026. Search terms covered PE, CTPA, NCCT, AI, deep learning, machine learning, and related concepts. Eligible evidence included peer-reviewed original studies, validation studies, and peer-reviewed conference proceedings reporting AI methods, quantitative findings, or clinically relevant methodological insights.
Key content and findings: AI for CTPA has progressed from computer-aided detection toward segmentation, clot burden quantification, workflow triage, and risk stratification, with encouraging performance in selected retrospective and real-world studies. AI for NCCT remains exploratory and includes indirect sign analysis, direct detection, synthetic contrast generation, and…
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