Machine Learning Approach for Enumeration of Circulating Cells with Diffuse in vivo Flow Cytometry

Diffuse in vivo flow cytometry (DiFC) is an emerging technique for enumerating rare, fluorescently-labeled circulating tumor cells (CTCs) in small animals without drawing blood samples. DiFC uses detection of transient fluorescent peaks in time-series data. Previously, we used a simple amplitude threshold-based method for identifying peak candidates, but it ignores potentially useful information in peak shape that could reduce false-positive detections from instrument noise and increase detection efficiency of lower-amplitude peaks.

To develop a machine learning (ML)-integrated signal processing approach for improved CTC enumeration using DiFC by distinguishing CTC peaks from artifacts.

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 in-silico, control, and CTC-bearing mouse datasets.

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.

The ML-integrated approach significantly improves DiFC CTC enumeration, enabling robustness against artifacts in noisy conditions.