FAN-enhanced vision transformer and distributed bidirectional optical flow for heart failure detection from echocardiography videos

Heart failure (HF) is a major cardiovascular condition that requires early and accurate diagnosis to improve patient outcomes. Echocardiography is widely used for cardiac assessment, however, the analysis of echocardiography videos using deep learning models often suffers from high computational complexity and redundant temporal information, limiting applicability in real-time clinical environments. To address these challenges, this study proposes a FAN-enhanced Vision Transformer integrated with distributed bidirectional optical flow for automatic heart failure detection from echocardiography videos. The proposed method introduces a preprocessing pipeline consisting of a frame refocusing algorithm to suppress stationary pixels, distance-based frame selection to reduce redundant frames, and distributed bidirectional optical flow to pad frame sequences. Furthermore, the Vision Transformer architecture is enhanced with Fourier Analysis Network (FAN) layers, enabling the model to capture periodic cardiac motion patterns while maintaining computational efficiency. Experimental evaluations were conducted on three standard echocardiography views. The proposed method achieved its best performance on the A2C view with an accuracy of 95.95% and a normalized Matthews Correlation Coefficient (MCC) of 96.02%. This result is statistically significant and better than other state-of-the-art methods. This approach provides a more robust and efficient diagnostic, as the FAN layers capture cardiac periodicity while requiring fewer parameters.