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Artificial neural network-based model enhances risk stratification and reduces non-invasive cardiac stress imaging
Hussain A Isma'eel1,2, George E Sakr3, Mustapha Serhan4
1Division of Cardiology, Department of Internal Medicine, American University of Beirut, PO-BOX 11-0236, Riad el Solh, Beirut, 11072020, Lebanon. hi09@aub.edu.lb.
Insights
Artificial neural networks (ANN) improve risk stratification for coronary artery disease (CAD) detection. ANN models offer a 98% negative predictive value, potentially reducing unnecessary non-invasive imaging tests.
Area of Science:
- Cardiology
- Artificial Intelligence in Medicine
- Medical Diagnostics
Background:
- Coronary artery disease (CAD) is a leading cause of cardiovascular events.
- Stress testing is crucial for non-invasive CAD assessment.
- Enhanced risk stratification is needed for patients undergoing stress testing.
Purpose of the Study:
- To compare artificial neural network (ANN)-based prediction models with existing risk models (Diamond-Forrester and Morise) for CAD.
- To evaluate the accuracy and discriminatory power of ANN models in predicting ischemia.
Main Methods:
- Prospective recruitment of 486 patients aged 19+ undergoing imaging-based stress tests for CAD evaluation.
- Systematic ANN architecture development with incremental neuron changes and bootstrapping for accuracy assessment.
Main Results:
- The ANN model demonstrated higher discriminatory power (1.61) compared to Diamond-Forrester and Morise models.
- ANN achieved a 98% negative predictive value, 91% sensitivity, and 65% specificity for ischemia prediction.
- A potential 59% reduction in non-invasive imaging was observed with the ANN model.
Conclusions:
- ANN models significantly improve risk stratification for CAD compared to traditional risk scores.
- The high negative predictive value of ANN models can lead to a substantial reduction in non-invasive imaging procedures.
Background:
Coronary artery disease (CAD) accounts for more than half of all cardiovascular events. Stress testing remains the cornerstone for non-invasive assessment of patients with possible or known CAD. Clinical utilization reviews show that most patients presenting for evaluation of stable CAD by stress testing are categorized as low risk prior to the test. Attempts to enhance risk stratification of individuals who are sent for stress testing seem to be more in need today. The present study compares artificial neural networks (ANN)-based prediction models to the other risk models being used in practice (the Diamond-Forrester and the Morise models).
Methods:
In our study, we prospectively recruited patients who were 19 years of age or older, and were being evaluated for coronary artery disease with imaging-based stress tests. For ANN, the network architecture employed a systematic method, where the number of neurons is changed incrementally, and bootstrapping was performed to evaluate the accuracy of the models.
Results:
We prospectively enrolled 486 patients. The mean age of patients undergoing stress test was 55.2 ± 11.2 years, 35% were women, and 12% had a positive stress test for ischemic heart disease. When compared to Diamond-Forrester and Morise risk models, the ANN model for predicting ischemia provided higher discriminatory power (DP)(1.61), had a negative predictive value of 98%, Sensitivity 91% [81%-97%], Specificity 65% [60%-79%], positive predictive value 26%, and a potential 59% reduction of non-invasive imaging.
Conclusion:
The ANN models improved risk stratification when compared to the other risk scores (Diamond-Forrester and Morise) with a 98% negative predictive value and a significant potential reduction in non-invasive imaging tests.
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