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Novel deep learning method for coronary artery tortuosity detection through coronary angiography
Miriam Cobo1, Francisco Pérez-Rojas2,3, Constanza Gutiérrez-Rodríguez4
1Advanced Computing and e-Science Research Group, Institute of Physics of Cantabria (IFCA), CSIC - UC, 39005, Santander, Cantabria, Spain. cobocano@ifca.unican.es.
Insights
Artificial intelligence, specifically deep learning, can now automatically detect coronary artery tortuosity from angiograms. This AI tool shows performance comparable to human experts, aiding in crucial interventional treatment planning.
Area of Science:
- Cardiology
- Medical Imaging
- Artificial Intelligence
Background:
- Coronary artery tortuosity is often undetected during coronary angiography, complicating treatment planning.
- Accurate assessment of coronary artery morphology is vital for interventions like stenting.
Purpose of the Study:
- To develop an artificial intelligence algorithm for automatic detection of coronary artery tortuosity using coronary angiography.
- To analyze coronary artery tortuosity with deep learning techniques.
Main Methods:
- Utilized deep learning, specifically convolutional neural networks (CNNs), to classify coronary angiographies as tortuous or non-tortuous.
- Trained the model on 658 left (Spider) and right (45°/0°) coronary angiographies using fivefold cross-validation.
Main Results:
- The AI system achieved a test accuracy of 87% ± 6% for detecting coronary artery tortuosity.
- The model demonstrated a mean area under the curve of 0.96 ± 0.03, with sensitivity and specificity comparable to expert radiologists.
Conclusions:
- Deep learning CNNs offer a promising, automated approach for detecting coronary artery tortuosity.
- This AI-driven system has significant potential applications in cardiology and medical imaging for improved patient care.
Abstract:
Coronary artery tortuosity is usually an undetected condition in patients undergoing coronary angiography. This condition requires a longer examination by the specialist to be detected. Yet, detailed knowledge of the morphology of coronary arteries is essential for planning any interventional treatment, such as stenting. We aimed to analyze coronary artery tortuosity in coronary angiography with artificial intelligence techniques to develop an algorithm capable of automatically detecting this condition in patients. This work uses deep learning techniques, in particular, convolutional neural networks, to classify patients into tortuous or non-tortuous based on their coronary angiography. The developed model was trained both on left (Spider) and right (45°/0°) coronary angiographies following a fivefold cross-validation procedure. A total of 658 coronary angiographies were included. Experimental results demonstrated satisfactory performance of our image-based tortuosity detection system, with a test accuracy of (87 ± 6)%. The deep learning model had a mean area under the curve of 0.96 ± 0.03 over the test sets. The sensitivity, specificity, positive predictive values, and negative predictive values of the model for detecting coronary artery tortuosity were (87 ± 10)%, (88 ± 10)%, (89 ± 8)%, and (88 ± 9)%, respectively. Deep learning convolutional neural networks were found to have comparable sensitivity and specificity with independent experts' radiological visual examination for detecting coronary artery tortuosity for a conservative threshold of 0.5. These findings have promising applications in the field of cardiology and medical imaging.
