Rapid prediction of organisation in engineered corneal, glial and fibroblast tissues using machine learning and biophysical models
The episode discusses a paper introducing RAPTOR, a machine learning approach using pix2pix to predict cell organization in engineered corneal, glial, and fibroblast tissues. The hosts explore how this tool links biophysical models like CONDOR simulations with deep learning to predict tissue structure. They conclude that RAPTOR is a validated tool for designing scaffolds by rapidly identifying physical parameters from experimental data.