A convolutional neural network incorporating temporal attention mechanisms demonstrates enhanced accuracy in detecting coronary artery disease from angiogram sequences, potentially identifying subtle disease patterns that conventional analysis misses. The model processes sequential medical imaging data to focus on relevant temporal patterns across different disease stages. This represents a meaningful advance in cardiovascular AI diagnostics, addressing a critical healthcare need given CAD's status as a leading cause of death affecting over 300 million people globally. The temporal attention component is particularly notable, as it could help detect early-stage disease when interventions are most effective. However, several limitations warrant consideration. The abstract lacks specific performance metrics, making it difficult to assess the magnitude of improvement over existing methods. Clinical validation details are absent, and integration challenges into real-world workflows remain unclear. As this is a preprint awaiting peer review, the methodology and results require independent verification before clinical implementation. While promising for improving early CAD detection, the practical impact will depend on rigorous validation studies and successful integration into existing diagnostic pathways.