Abstract
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 CTPA images of early-stage COVID-19 patients.
This prospective single-center 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) was used to estimate the quantity of opacities. Visual quantitative assessments were performed independently by two radiologists.
There was a positive correlation between radiologist estimations (r2 = 0.57) and between the AI data and the mean of the radiologists’ estimations (r2 = 0.70). Bland-Altman plot analysis showed a mean bias of + 3.06% between radiologists and -1.32% between the mean radiologist vs AI, with no outliers outside 2xSD for respective comparison.
The AI protocol facilitated a quantitative assessment of lung opacities and showed a strong correlation with data obtained from two independent radiologists, demonstrating its potential as a complementary tool in clinical practice.
In assessing COVID-19 lung opacities in CTPA images, AI tools trained on non-contrast images, provide comparable results to visual assessments by radiologists.
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.
Accepted manuscripts
