Workflow insights can enable safety- and efficiency improvements in the Cardiac Catheterisation Laboratory (Cath Lab). Human pose tracklets from video footage can provide a source of workflow information. However, occlusions and visual similarity between personnel make the Cath Lab a challenging environment for the re-identification of individuals. We propose a human pose tracker that addresses these problems specifically, and test it on recordings of real coronary angiograms. This tracker uses no visual information for re-identification, and instead employs object keypoint similarity between detections and predictions from a third-order motion model. Algorithm performance is measured on Cath Lab footage using Higher-Order Tracking Accuracy (HOTA). To evaluate its stability during procedures, this is done separately for five different surgical steps of the procedure. We achieve up to 0.71 HOTA where tested state-of-the-art pose trackers score up to 0.65 on the used dataset. We observe that the pose tracker HOTA performance varies with up to 10 percentage point (Image 1) between workflow phases, where tested state-of-the-art trackers show differences of up to Image 2. In addition, the tracker achieves up to 22.5 frames per second, which is 9 frames per second faster than the current state-of-the-art on our setup in the Cath Lab. The fast and consistent short-term performance of the provided algorithm makes it suitable for use in workflow analysis in the Cath Lab and opens the door to real-time use-cases.@en

This study evaluates the performance of deep learning models in the prediction of the end time of procedures performed in the cardiac catheterization laboratory (cath lab). We employed only the clinical phases derived from video analysis as input to the algorithms. Our results show that InceptionTime and LSTM-FCN yielded the most accurate predictions. InceptionTime achieves Mean Absolute Error (MAE) values below 5 min and Symmetric Mean Absolute Percentage Error (SMAPE) under 6% at 60-s sampling intervals. In contrast, LSTM with attention mechanism and standard LSTM models have higher error rates, indicating challenges in handling both long-term and short-term dependencies. CNN-based models, especially InceptionTime, excel at feature extraction across different scales, making them effective for time-series predictions. We also analyzed training and testing times. CNN models, despite higher computational costs, significantly reduce prediction errors. The Transformer model has the fastest inference time, making it ideal for real-time applications. An ensemble model derived by averaging the two best performing algorithms reported low MAE and SMAPE, although needing longer training. Future research should validate these findings across different procedural contexts and explore ways to optimize training times without losing accuracy. Integrating these models into clinical scheduling systems could improve efficiency in cath labs. Our research demonstrates that the models we implemented can form the basis of an automated tool, which predicts the optimal time to call the next patient with an average error of approximately 30 s. These findings show the effectiveness of deep learning models, especially CNN-based architectures, in accurately predicting procedure end times.

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Workflow insights can improve efficiency and safety in the Cardiac Catheterization Laboratory (Cath Lab). As manual analysis is labor-intensive, we aim for automation through camera monitoring. Literature shows that human poses are indicative of activities and therefore workflow. As a first exploration, we evaluate how marker-less multi-human pose estimators perform in the Cath Lab. We annotated poses in 2040 frames from ten multi-view coronary angiogram (CAG) recordings. Pose estimators AlphaPose, OpenPifPaf and OpenPose were run on the footage. Detection and tracking were evaluated separately for the Head, Arms, and Legs with Average Precision (AP), head-guided Percentage of Correct Keypoints (PCKh), Association Accuracy (AA), and Higher-Order Tracking Accuracy (HOTA). We give qualitative examples of results for situations common in the Cath Lab, with reflections in the monitor or occlusion of personnel. AlphaPose performed best on most mean Full-pose metrics with an AP from 0.56 to 0.82, AA from 0.55 to 0.71, and HOTA from 0.58 to 0.73. On PCKh OpenPifPaf scored highest, from 0.53 to 0.64. Arms, Legs, and the Head were detected best in that order, from the views which see the least occlusion. During tracking in the Cath Lab, AlphaPose tended to swap identities and OpenPifPaf merged different individuals. Results suggest that AlphaPose yields the most accurate confidence scores and limbs, and OpenPifPaf more accurate keypoint locations in the Cath Lab. Occlusions and reflection complicate pose tracking. The AP of up to 0.82 suggests that AlphaPose is a suitable pose detector for workflow analysis in the Cath Lab, whereas its HOTA of up to 0.73 here calls for another tracking solution.@en