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Evaluation of an AI-based, automatic coronary artery calcium scoring software
Mårten Sandstedt1,2, Lilian Henriksson3,4, Magnus Janzon5
1Center for Medical Image Science and Visualization (CMIV), Linköping University, Linköping, Sweden. martensandstedt@gmail.com.
European Radiology
|November 16, 2019
Summary
An artificial intelligence (AI) software for coronary artery calcium (CAC) scoring shows excellent accuracy and agreement compared to traditional methods. This AI tool is also significantly faster, making it a promising advancement in clinical settings.
Area of Science:
- Cardiology
- Radiology
- Artificial Intelligence in Medicine
Background:
- Coronary artery calcium (CAC) scoring is crucial for cardiovascular risk assessment.
- Current semi-automatic methods require significant user time and expertise.
- The development of automated CAC scoring tools is essential for clinical efficiency.
Purpose of the Study:
- To evaluate the performance of an artificial intelligence (AI)-based automatic coronary artery calcium (CAC) scoring software.
- To compare the AI software's results against a semi-automatic software as a reference standard.
- To assess the time efficiency of the automated CAC scoring method.
Main Methods:
- An observational study analyzed 315 non-contrast-enhanced calcium scoring computed tomography (CSCT) scans.
- Agatston score (AS), volume score (VS), mass score (MS), and lesion count were calculated using both semi-automatic and automatic software.
- Statistical analyses included correlation (Spearman's ⍴), agreement (ICC, Bland Altman plots), and time analysis.
Main Results:
- Excellent correlation (⍴ > 0.93) and agreement (ICC > 0.99) were observed for AS, VS, and MS between the two methods.
- High correlation (⍴ = 0.903) and agreement (ICC = 0.977) were found for the number of calcified lesions.
- The automatic method was significantly faster (36s vs 59s, p < 0.001) and demonstrated good agreement in risk category assignment (κ = 0.919).
Conclusions:
- AI-based automatic CAC scoring software demonstrates excellent correlation and agreement with semi-automatic methods.
- The automated software is a time-efficient alternative for CAC scoring in clinical practice.
- While accurate, the AI software showed a tendency for overestimation in risk category assignment.
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