Assessing the diagnostic accuracy and prognostic utility of artificial intelligence detection and grading of coronary artery calcification on nongated computed tomography (CT) thorax.
Authors
Affiliations (5)
Affiliations (5)
- University of Bristol, Beacon House, Queens Rd, Bristol BS8 1QU, UK.
- Department of Cardiology, Royal United Hospital, Combe Park, Bath, BA1 3NG, UK; Department for Health, University of Bath, Claverton Down, Bath, BA2 7AY, UK.
- Department of Cardiology, Royal United Hospital, Combe Park, Bath, BA1 3NG, UK.
- Department of Radiology, Royal United Hospital, Combe Park, Bath, Avon, BA1 3NG, UK.
- Department for Health, University of Bath, Claverton Down, Bath, BA2 7AY, UK; Department of Radiology, Royal United Hospital, Combe Park, Bath, Avon, BA1 3NG, UK. Electronic address: [email protected].
Abstract
This study assessed the diagnostic accuracy and prognostic implications of an artificial intelligence (AI) tool for coronary artery calcification (CAC) assessment on nongated, noncontrast thoracic computed tomography (CT). A single-centre retrospective analysis of 75 consecutive patients per age group (<40, 40-49, 50-59, 60-69, 70-79, 80-89, and ≥90 years) undergoing non-gated, non-contrast CT (January-December 2015) was conducted. AI analysis reported CAC presence and generated an Agatston score, and the performance was compared with baseline CT reports and a dedicated radiologist re-review. Interobserver variability between AI and radiologist assessments was measured using Cohen's κ. All-cause mortality was recorded, and its association with AI-detected CAC was tested. A total of 291 patients (mean age: 64 ± 19, 51% female) were included, with 80% (234/291) of AI reports passing radiologist quality assessment. CAC was reported on 7% (17/234) of initial clinical reports, 58% (135/234) on radiologist re-review, and 57% (134/234) by AI analysis. After manual quality assurance (QA) assessment, the AI tool demonstrated high sensitivity (96%), specificity (96%), positive predictive value (95%), and negative predictive value (97%) for CAC detection compared with radiologist re-review. Interobserver agreement was strong for CAC prevalence (κ = 0.92) and moderate for severity grading (κ = 0.60). AI-detected CAC presence and severity predicted all-cause mortality (p < 0.001). The AI tool exhibited feasible analysis potential for non-contrast, non-gated thoracic CTs, offering prognostic insights if integrated into routine practice. Nonetheless, manual quality assessment remains essential. This AI tool represents a potential enhancement to CAC detection and reporting on routine noncardiac chest CT.