Unsupervised multi-modal variational autoencoder for anomaly detection in coronary angiography

Coronary angiography (CAG) is among the most common procedures in catheterization laboratories. Patient-specific vessel tortuosity may require additional catheter exchanges, increasing procedure duration and complication risk. Detecting such workflow disruptions automatically is challenging because anomalies are subtle and difficult to capture using a single data modality.

We propose a novel unsupervised multi-modal anomaly detection framework based on a variational autoencoder (VAE). Video and pose data from 191 real-world CAG procedures were segmented into 10-second clips. Features extracted with 3D convolutional networks were fused through modality-specific LSTM encoders. Reconstruction error (mean squared error) was used as the anomaly score, and thresholds were calibrated on a held-out dataset.

Procedures requiring additional catheter exchanges accounted for 29.8% of the dataset and had a 34% longer mean duration than normal procedures (47.6 vs. 35.5 min). The multi-modal VAE outperformed unimodal models, achieving an F1-score of 0.83, AUC-ROC of 0.88, and AUC-PR of 0.90. Detection performance peaked during the period when anomalies most frequently occurred.

This study demonstrates the effectiveness of multi-modal fusion for unsupervised anomaly detection in complex surgical workflows. These findings support the development of intelligent workflow monitoring systems for catheterization laboratories.