Real‐Time, Label‐Free Classification of Cell Death Pathways via Holotomography‐Based Deep Learning Framework

Accurate and quantitative classification of cell death pathways is fundamental for elucidating disease mechanisms and assessing therapeutic efficacy, as dysregulated cell death underlies a wide range of pathological conditions including cancer and therapy resistance. However, conventional imaging methods such as fluorescence and bright‐field microscopy, or 2D phase imaging, often suffer from phototoxicity, labeling artifacts, or limited morphological contrast. Herein, a real‐time, label‐free platform for classifying cell death phenotypes—apoptosis, necroptosis, and necrosis—is introduced by combining 3D holotomography with deep learning. A convolutional neural network, trained on refractive index (RI)‐based features from HeLa cells, achieves high classification accuracy (99.3%) under varying cell densities. Notably, the model detected early RI changes during necroptosis several hours before conventional fluorescence‐based markers, validated by population‐level comparison with flow cytometry. The framework's adaptability is demonstrated by successfully fine‐tuning the model for A549 lung cancer cells. Collectively, these findings demonstrate the potential of HT‐based AI as a universal, high‐resolution, and label‐free platform for quantitative cell death profiling and translational drug‐response analysis.