Human AI teaming for Coronary CT angiography assessment: impact on imaging workflow and diagnostic accuracy
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 diagnosti...
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| Hauptverfasser: | , , , , , , , , , , , , |
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| Dokumenttyp: | Article (Journal) |
| Sprache: | Englisch |
| Veröffentlicht: |
30 November 2023
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| In: |
Diagnostics
Year: 2023, Jahrgang: 13, Heft: 23, Pages: 1-10 |
| ISSN: | 2075-4418 |
| DOI: | 10.3390/diagnostics13233574 |
| Online-Zugang: | Verlag, kostenfrei, Volltext: https://doi.org/10.3390/diagnostics13233574 Verlag, kostenfrei, Volltext: https://www.mdpi.com/2075-4418/13/23/3574 
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| Verfasserangaben: | Florian Andre, Philipp Fortner, Matthias Aurich, Sebastian Seitz, Ann-Kathrin Jatsch, Max Schöbinger, Michael Wels, Martin Kraus, Mehmet Akif Gülsün, Norbert Frey, Andre Sommer, Johannes Görich and Sebastian J. Buss |
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| 520 | |a 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. | ||
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