Intrinsic dimensionality of deep learning representations reveals cell death-associated heterogeneity in Parkinsons disease iPSC-derived neurons

As AI is increasingly used to stratify heterogeneous Parkinsons disease, it is essential to determine whether learned representations preserve disease-relevant variation within diagnostic or genetic groups. In iPSC-derived neurons from three familial PD cases, deep learning representations encoded an independent measure of cell death, and their intrinsic dimensionality increased with death burden. Latent dimensionality offers a way to examine heterogeneity before AI-derived clusters are interpreted as disease subtypes.