Machine Learning Approach for Enumeration of Circulating Cells with Diffuse

Significance: Diffuse Aim: To develop a machine learning (ML)-integrated signal processing approach for improved CTC enumeration using DiFC by distinguishing CTC peaks from artifacts. Approach: We developed an ML-integrated approach that incorporates a convolutional neural network (CNN) classifier. The CNN was trained to distinguish CTC peaks from artifacts by analyzing peak amplitude and temporal shape characteristics. Performance was validated on Results: The CNN classifier achieved accuracy, precision, sensitivity, and specificity exceeding 98% on test data. Compared with our previously published threshold-based approach, the ML-integrated method increased the number of correctly identified CTCs and their flow direction while reducing false detections across validation datasets. Conclusions: The ML-integrated approach significantly improves DiFC CTC enumeration, enabling robustness against artifacts in noisy conditions.