IAR‐Net: Tabular Deep Learning Model for Interventionalist's Action Recognition

Interventionalist catheterization actions are essential for assessing tool navigation quality and procedural competence during interventions. Traditional assessment methods are subjective, lack immediate feedback, and limit timely performance improvement. To address these limitations, this study introduces a deep‐learning framework designed to systematically analyze catheterization action data, address inherent class imbalances, and enable real‐time action recognition. First, the proposed framework leverages advanced generative models to augment minority action classes, thus enhancing data representation and ensuring accurate recognition of catheterization actions. The six generative models utilized in this study undergo rigorous evaluation, achieving high fidelity with average precision and F1‐scores exceeding 94% across all models except CTGAN. Second, a convolutional neural network (IAR‐Net) tailored to recognize seven distinct catheterization actions is developed. Evaluated using the augmented dataset, IAR‐Net achieves an accuracy of 98.9%, surpassing current benchmarks. Comparative analysis with state‐of‐the‐art machine learning and transformer‐based models designed for tabular data confirms IAR‐Net's performance and robustness in recognizing catheterization actions. Lastly, interpretability methods are incorporated to elucidate the model's decision‐making process, improving understanding and increasing the trustworthiness of predictions. These outcomes offer a promising avenue for enhancing trainee assessment and training protocols, thereby accelerating the acceptance and integration of robot‐assisted endovascular systems into clinical practice.