Deep Learning of Position-Aligned Dynamic Cell Deformation Trajectories for Ovarian Cell Phenotyping in Hyperbolic...

Cell deformation during microfluidic transport evolves continuously, whereas cell phenotyping commonly relies on geometric descriptors measured at one or a few predefined channel positions. Such fixed-position measurements capture selected deformation states but overlook how the cellular response progresses along the flow path. Combined with high-speed imaging, hyperbolic microchannels capture this progression as a continuous trajectory, enabling the shift from static morphological measurements to a dynamic response analysis. However, cell-to-cell differences in transit velocity cause equivalent image frames to represent different channel positions and hydrodynamic conditions, preventing a direct comparison of frame-indexed deformation trajectories. Here, we develop a trajectory-resolved framework that converts high-speed microfluidic image sequences into position-aligned dynamic cell deformation trajectories and uses deep learning to model their cell line-specific evolution for label-free ovarian cell phenotyping. Geometric and kinematic trajectories were extracted from three ovarian cancer cell lines (A2780, OVCAR-3, and SKOV-3) and one nonmalignant ovarian epithelial cell line (IOSE-80) at three flow rates. The trajectories were reparameterized by the axial position and resampled on a common spatial grid. Class-specific one-dimensional convolutional neural networks predicted downstream trajectories from the inlet state and flow conditions. An unknown cell was classified by comparing its measured trajectory with class-specific predictions, and prediction errors in the width, area, perimeter, and axial velocity were integrated by soft voting. The framework achieved 93.26% accuracy, 94.07% macro precision, 90.25% macro recall, and a 91.85% macro-F1 score on the held-out test data. These results support extending microfluidic cell phenotyping from isolated geometric states to the position-aligned analysis of dynamic deformation trajectories.