Fully automated artificial intelligence-based coronary CT angiography image processing: efficiency, diagnostic capability, and risk stratification.
Eur Radiol
; 34(8): 4909-4919, 2024 Aug.
Article
in En
| MEDLINE
| ID: mdl-38193925
ABSTRACT
OBJECTIVES:
To prospectively investigate whether fully automated artificial intelligence (FAAI)-based coronary CT angiography (CCTA) image processing is non-inferior to semi-automated mode in efficiency, diagnostic ability, and risk stratification of coronary artery disease (CAD). MATERIALS ANDMETHODS:
Adults with indications for CCTA were prospectively and consecutively enrolled at two hospitals and randomly assigned to either FAAI-based or semi-automated image processing using equipment workstations. Outcome measures were workflow efficiency, diagnostic accuracy for obstructive CAD (≥ 50% stenosis), and cardiovascular events at 2-year follow-up. The endpoints included major adverse cardiovascular events, hospitalization for unstable angina, and recurrence of cardiac symptoms. The non-inferiority margin was 3 percentage difference in diagnostic accuracy and C-index.RESULTS:
In total, 1801 subjects (62.7 ± 11.1 years) were included, of whom 893 and 908 were assigned to the FAAI-based and semi-automated modes, respectively. Image processing times were 121.0 ± 18.6 and 433.5 ± 68.4 s, respectively (p <0.001). Scan-to-report release times were 6.4 ± 2.7 and 10.5 ± 3.8 h, respectively (p < 0.001). Of all subjects, 152 and 159 in the FAAI-based and semi-automated modes, respectively, subsequently underwent invasive coronary angiography. The diagnostic accuracies for obstructive CAD were 94.7% (89.9-97.7%) and 94.3% (89.5-97.4%), respectively (difference 0.4%). Of all subjects, 779 and 784 in the FAAI-based and semi-automated modes were followed for 589 ± 182 days, respectively, and the C-statistic for cardiovascular events were 0.75 (0.67 to 0.83) and 0.74 (0.66 to 0.82) (difference 1%).CONCLUSIONS:
FAAI-based CCTA image processing significantly improves workflow efficiency than semi-automated mode, and is non-inferior in diagnosing obstructive CAD and risk stratification for cardiovascular events. CLINICAL RELEVANCE STATEMENT Conventional coronary CT angiography image processing is semi-automated. This observation shows that fully automated artificial intelligence-based image processing greatly improves efficiency, and maintains high diagnostic accuracy and the effectiveness in stratifying patients for cardiovascular events. KEY POINTS ⢠Coronary CT angiography (CCTA) relies heavily on high-quality and fast image processing. ⢠Full-automation CCTA image processing is clinically non-inferior to the semi-automated mode. ⢠Full automation can facilitate the application of CCTA in early detection of coronary artery disease.Key words
Full text:
1
Collection:
01-internacional
Database:
MEDLINE
Main subject:
Coronary Artery Disease
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Artificial Intelligence
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Coronary Angiography
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Computed Tomography Angiography
Type of study:
Diagnostic_studies
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Etiology_studies
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Prognostic_studies
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Risk_factors_studies
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Screening_studies
Limits:
Aged
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Female
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Humans
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Male
/
Middle aged
Language:
En
Journal:
Eur Radiol
Journal subject:
RADIOLOGIA
Year:
2024
Type:
Article
Affiliation country:
China