Key result
3D-CNN predicts abnormal cardiac index from routine coronary angiogram videos with an AUC of 0.83.
Why the study?
Assessing cardiac index during coronary angiography can improve decision-making, but requires an additional invasive procedure and is often omitted.
Does a 3D-CNN model accurately predict cardiac index from coronary angiogram videos in patients undergoing coronary angiography?
Observational (n=9,103)
No
Does a 3D-CNN model accurately predict cardiac index from coronary angiogram videos in patients undergoing coronary angiography?
Effect estimate: AUC 0.83 (95% CI 0.81 to 0.85)
A 3D-CNN model can feasibly predict cardiac index from routine coronary angiogram videos with good accuracy, potentially providing real-time hemodynamic data without additional invasive procedures.
Objective: Data on cardiac index (CI) in patients undergoing coronary angiography (CAG) can enhance decision-making and improve clinical outcomes. However, this necessitates an additional invasive procedure, and therefore, it is frequently omitted. This study explores the novel application of convolutional neural networks (CNNs) to predict CI from routine CAGs. Methods and analysis: Patients who underwent CAG and simultaneous right heart catheterisation at Mayo Clinic between 2002 and 2023 were included. A three-dimensional (3D)-CNN model (X3D architecture) was developed to predict binary CI categories (normal: CI≥2.2 vs abnormal: CI<2.2 L/min/m²) from CAG videos. CAGs were randomly split into training (70%), validation (15%) and test (15%) sets. All available videos from each study were entered into the model. The model's performance was evaluated using the area under the curve (AUC), sensitivity, specificity and F1 score. Results: A total of 15 297 CAG studies were considered. After applying a quality-check algorithm to exclude low-quality or irrelevant CAGs, 11 475 studies from 9103 patients were included. CI was abnormal in 25.2% of studies. The model achieved an AUC of 0.83 (95% CI 0.81 to 0.85), sensitivity of 0.72 (95% CI 0.68 to 0.77), specificity of 0.78 (95% CI 0.75 to 0.80) and F1 score of 0.62 (95% CI 0.58 to 0.65) on the test dataset. Limiting the input to 4, 3 or 2 prespecified CAG videos representing standard views did not significantly affect the model's performance, supporting its applicability in varied clinical workflows. Conclusions: This study demonstrates the feasibility of leveraging deep learning models to predict CI from CAG videos. The proposed 3D-CNN model shows promise as a tool for providing physicians with real-time data to aid decision-making during CAG procedures.
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Chang et al. (2026) conducted an observational in Patients undergoing coronary angiography (n=9,103). 3D convolutional neural network (3D-CNN) model was evaluated on Prediction of binary cardiac index categories (normal: CI≥2.2 vs abnormal: CI<2.2 L/min/m²) (AUC 0.83, 95% CI 0.81 to 0.85). A 3D convolutional neural network predicted abnormal cardiac index (<2.2 L/min/m²) from routine coronary angiogram videos with an AUC of 0.83 (95% CI 0.81 to 0.85).
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