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Scott A. Small M.D.
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Machine-learning algorithm to improve disease diagnosis using magnetic resonance spectroscopy
CU24256
This technology is a neural network architecture that quantifies in vivo metabolites from magnetic resonance spectroscopy for improved disease detection and diagnosis.
This technology is a set of metabolite profiles for identifying disruptions in the brain glutamate metabolic cycle that can enable more targeted treatment of schizophrenia and related psychotic disorders.
This technology is a diagnostic tool for schizophrenia using magnetic resonance spectroscopy (MRS) for detecting elevated metabolite levels in the brain.