"Relevance vector machine" consciousness classifier applied to cerebral metabolism of vegetative and locked-in patients.
Neuroimage May 15, 2011 Christophe L Phillips, Marie-Aurélie Bruno, Pierre Maquet et al.
A machine-learning classifier trained on fluorodeoxyglucose PET brain scans from 37 healthy controls and 13 patients in a vegetative state achieved 100% accuracy in distinguishing between conscious awareness and the vegetative state during cross-validation. When tested on 8 patients with locked-in syndrome, all scans were classified as "conscious" with a mean probability of .95. The authors conclude that relevance vector machine classification of cerebral metabolic images could become a useful tool for automated PET-based diagnosis of altered states of consciousness in coma survivors.