Abstract: A non-invasive computer-aided diagnosis system generates a diagnosis of mild cognitive impairment, a disease state which often leads to the development of Alzheimer's disease. The system uses as inputs both functional positron emission tomography and structural magnetic resonance imaging data, reconstructs a model of the patient's cortex, uses machine-learning techniques to generate probabilities for mild cognitive impairments for local cortical regions, uses machine-learning techniques to fuse the local diagnoses to generate a global diagnosis based on each imaging modality, then uses machine-learning techniques to fuse the modality-specific global diagnoses to generate a final global diagnosis.

Abstract: A non-invasive computer-aided diagnosis system generates a diagnosis of mild cognitive impairment, a disease state which often leads to the development of Alzheimer's disease. The system uses as inputs both functional positron emission tomography and structural magnetic resonance imaging data, reconstructs a model of the patient's cortex, uses machine-learning techniques to generate probabilities for mild cognitive impairments for local cortical regions, uses machine-learning techniques to fuse the local diagnoses to generate a global diagnosis based on each imaging modality, then uses machine-learning techniques to fuse the modality-specific global diagnoses to generate a final global diagnosis.