Deep Learning-Based Quantification of Residual Blood Clots in Single-Use Dialyzers Using Bedside Mobile-Captured Images

Our approach effectively detects residual blood clots in dialyzers using ConvNeXt by leveraging image data from both ends. The use of explainable AI tools confirmed the model's ability to accurately identify blood clots by focusing on relevant regions. Our study emphasizes the need to balance model complexity with computational efficiency. The ConvNeXt-Base model successfully avoided overfitting while maintaining practical performance, which could lead to improved clinical decision-making by minimizing circuit downtime and optimizing anemia management.