November 30, 2023DiagnosticsOpen Access

Human AI Teaming for Coronary CT Angiography Assessment: Impact on Imaging Workflow and Diagnostic Accuracy

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Why the study?

With coronary CTA volumes expected to rise, technologies like artificial intelligence are needed to optimize imaging workflow and diagnostic accuracy.

Does AI-based coronary analysis reduce assessment and reporting time compared to standard analysis in patients undergoing coronary CTA?

Structured PICO

Does AI-based coronary analysis reduce assessment and reporting time compared to standard analysis in patients undergoing coronary CTA?

Population

120 patients undergoing coronary CTA, median age 62.4 years, 79 men.

Intervention

AI-based (human AI) coronary analysis

Comparator

Standard coronary analysis

Outcome

Time for coronary CTA assessment and total reporting timesurrogate

AI-assisted coronary CTA analysis significantly reduces assessment and reporting time without compromising diagnostic accuracy.

Implication

May streamline CTA workflow in high-volume centers; leaves open whether AI teaming meaningfully boosts diagnostic accuracy.

Abstract

As the number of coronary computed tomography angiography (CTA) examinations is expected to increase, technologies to optimize the imaging workflow are of great interest. The aim of this study was to investigate the potential of artificial intelligence (AI) to improve clinical workflow and diagnostic accuracy in high-volume cardiac imaging centers. A total of 120 patients (79 men; 62.4 (55.0–72.7) years; 26.7 (24.9–30.3) kg/m2) undergoing coronary CTA were randomly assigned to a standard or an AI-based (human AI) coronary analysis group. Severity of coronary artery disease was graded according to CAD-RADS. Initial reports were reviewed and changes were classified. Both groups were similar with regard to age, sex, body mass index, heart rate, Agatston score, and CAD-RADS. The time for coronary CTA assessment (142.5 (106.5–215.0) s vs. 195.0 (146.0–265.5) s; p < 0.002) and the total reporting time (274.0 (208.0–377.0) s vs. 350 (264.0–445.5) s; p < 0.02) were lower in the human AI than in the standard group. The number of cases with no, minor, or CAD-RADS relevant changes did not differ significantly between groups (52, 7, 1 vs. 50, 8, 2; p = 0.80). AI-based analysis significantly improves clinical workflow, even in a specialized high-volume setting, by reducing CTA analysis and overall reporting time without compromising diagnostic accuracy.