Key result
Machine learning accurately estimates internal membrane potential from simulated capacitive current features.
Why the study?
Accurate and non-invasive measurement of cell membrane potential is essential for studying physiological processes and disease mechanisms, but current methods are limited.
Effect estimate: R2 = 0.90, RMSE = 13.79 mV
A simulation-based machine learning framework can accurately estimate membrane potential from capacitive current responses, offering a foundation for non-invasive measurements.
No takes yet. Share an insight, caveat, or question.
May support non-invasive membrane potential estimation; hypothesis-generating for clinical use, requiring in vivo validation.
A 2025 study studied this question. Machine learning model (XGBRegressor) using capacitive current features was evaluated on Estimation of internal potential (R2 = 0.90, RMSE = 13.79 mV). A machine learning model trained on simulated capacitive current features accurately estimated internal membrane potential (R2 = 0.90, RMSE = 13.79 mV).
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: