ArticleBJR open2025
Quantitative assessment of lung opacities from CT of pulmonary artery imaging data in COVID-19 patients: artificial intelligence versus radiologist.
Article in BJR open, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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9 authors.
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Abstract
Objectives: Artificial intelligence (AI) deep learning algorithms trained on non-contrast CT scans effectively detect and quantify acute COVID-19 lung involvement. Our study explored whether radiological contrast affects the accuracy of AI-measured lung opacities, potentially impacting clinical decisions. We compared lung opacity measurements from AI software with visual assessments by radiologists using CT pulmonary angiography (CTPA) images of early-stage COVID-19 patients. Methods: This prospective single-centre study included 18 COVID-19 patients who underwent CTPA due to suspected pulmonary embolism. Patient demographics, clinical data, and 30-day and 90-day mortality were recorded. AI tool (Pulmonary Density Plug-in, AI-Rad Companion Chest CT, SyngoVia; Siemens Healthineers, Forchheim, Germany) was used to estimate the quantity of opacities. Visual quantitative assessments were performed independently by 2 radiologists. Results: There was a positive correlation between radiologist estimations ( The AI protocol facilitated a quantitative assessment of lung opacities and showed a strong correlation with data obtained from 2 independent radiologists, demonstrating its potential as a complementary tool in clinical practice. Conclusion: In assessing COVID-19 lung opacities in CTPA images, AI tools trained on non-contrast images, provide comparable results to visual assessments by radiologists. Advances in knowledge: The Pulmonary Density Plug-in enables quantitative analysis of lung opacities in COVID-19 patients using contrast-enhanced CT images, potentially streamlining clinical workflows and supporting timely decision-making.
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