ML-Aided SERS Decodes Mitochondrial Changes in Aging Ovarian Cells

ML-assisted label-free SERS achieved 100% accuracy in discriminating senescent vs normal ovarian granulosa cells at the single-cell level, detecting DNA damage, protein changes, and lipid peroxidation in mitochondria. (No external validation reported.)

• Design: In vitro study using mitochondria-targeting gold nanobipyramids for SERS with machine learning classification; sample size not reported.
• Key secondary: Spectral signatures revealed DNA damage, protein conformational changes, and lipid peroxidation in senescent cells.
• Limitation: Single-cell level, potential overfitting, no external validation; clinical applicability unclear.

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