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Published on: September 22, 2023
Machine Learning-based Algorithm Enables the Exclusion of Obstructive Coronary Artery Disease in the Patients Who
Jan Głowacki1, Mateusz Krysiński2, Monika Czaja-Ziółkowska3
1Department of Diagnostic Imaging, Silesian Center for Heart Diseases, Curie-Skłodowskiej st. 9, 41-800 Zabrze, Poland; Department of Radiology, Silesian Medical University, Zabrze, Poland.
Insights
Artificial intelligence, using a gradient boosting machine (GBM) model, can effectively screen for obstructive coronary artery disease (CAD) after a coronary artery calcium scoring (CACS) test. This AI tool demonstrates high accuracy in ruling out obstructive CAD.
Area of Science:
- Cardiology
- Artificial Intelligence
- Medical Imaging
Background:
- Coronary artery disease (CAD) screening often involves coronary artery calcium scoring (CACS).
- Accurate identification of obstructive CAD is crucial for timely intervention.
- Existing methods may require further refinement for optimal patient stratification.
Purpose of the Study:
- To develop and evaluate an artificial intelligence (AI) model for screening obstructive coronary artery disease (CAD).
- To assess the predictive performance of a gradient boosting machine (GBM) model using CACS data.
- To improve the diagnostic accuracy after initial CACS testing.
Main Methods:
- A gradient boosting machine (GBM) model was developed using data from 435 patients with low to moderate CAD probability who underwent CACS and coronary computed tomography angiography.
- The GBM model was subsequently validated on an independent cohort of 126 patients.
- Model performance was evaluated using metrics such as sensitivity, specificity, and predictive values.
Main Results:
- The GBM model achieved 100% sensitivity and 69.8% specificity in cross-validation on the initial cohort.
- In the validation group, the model correctly identified 73 true negatives and 0 false negatives.
- The algorithm demonstrated a 100% negative predictive value (NPV) and 38% positive predictive value (PPV) for obstructive CAD.
Conclusions:
- The developed GBM algorithm shows significant potential for screening obstructive CAD after CACS.
- The model's high NPV suggests it is effective in excluding the presence of obstructive CAD.
- AI-driven analysis of CACS data can enhance the diagnostic pathway for coronary artery disease.
Rationale And Objectives:
An application of artificial intelligence to screen for obstructive coronary artery disease (CAD) after coronary artery calcium scoring (CACS) test.
Materials And Methods:
As an initial step we analyzed a group of 435 patients (23% male, mean age 61 ± 10) with low to moderate probability of CAD, who underwent clinically indicated CACS and coronary computed tomography angiography. Based on those data we elaborated a gradient boosting machine (GBM) model for prediction of obstructive CAD. Later the model was evaluated on a control group of 126 consecutive patients (31% male, mean age 59 ± 10).
Results:
Stratified 10-fold cross-validation performed on the group of 435 patients demonstrated the GBM model's sensitivity at 100 ± 0% and specificity at 69.8 ± 3.6%, while the outcomes (confusion matrix) of a clinical application on the group of 126 patients were: 73 true negative, 0 false negative, 20 true positive, and 33 false positive.
Conclusion:
The GBM algorithm showcased a considerably high discriminatory power for excluding the presence of obstructive CAD, with negative predictive value and positive predictive value of 100% and 38%, respectively.
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