Objective Artificial neural network is first used in the measurement study of brain of Alzheimer's disease using MRI, and a completely new pattern discriminating method is adopted, so as to take advantage of MRI ...Objective Artificial neural network is first used in the measurement study of brain of Alzheimer's disease using MRI, and a completely new pattern discriminating method is adopted, so as to take advantage of MRI to diagnose and identify AD patients. Methods 12 patients with probable AD (aged 65.33±8.62 years) and 36 normal controls matched with age and gender (aged 65.81±7.37 years) were studied. MRI are performed on Siemens Magnetom IMPACT 1.0 T; eight interesting brain structures including sixteen regions (left and right) indices are measured and studied; SPSS software and BP network software made by authors respectively were used to process and analyze the measured data. Results Using artificial neural network to the same regions and data, both the sensitivity and accuracy were found higher than using the traditional discrimination function analysis method; the indices of amygdala, hippocampus, parahippocampal gyrus, temporal lobe, and temporal horn, these five structures could completely differentiate AD from normal controls; new cases were successfully diagnosed. Conclusions Artificial neural network combining with MRI is probable to become a useful and reliable clinical tool to diagnose AD patients.展开更多
文摘Objective Artificial neural network is first used in the measurement study of brain of Alzheimer's disease using MRI, and a completely new pattern discriminating method is adopted, so as to take advantage of MRI to diagnose and identify AD patients. Methods 12 patients with probable AD (aged 65.33±8.62 years) and 36 normal controls matched with age and gender (aged 65.81±7.37 years) were studied. MRI are performed on Siemens Magnetom IMPACT 1.0 T; eight interesting brain structures including sixteen regions (left and right) indices are measured and studied; SPSS software and BP network software made by authors respectively were used to process and analyze the measured data. Results Using artificial neural network to the same regions and data, both the sensitivity and accuracy were found higher than using the traditional discrimination function analysis method; the indices of amygdala, hippocampus, parahippocampal gyrus, temporal lobe, and temporal horn, these five structures could completely differentiate AD from normal controls; new cases were successfully diagnosed. Conclusions Artificial neural network combining with MRI is probable to become a useful and reliable clinical tool to diagnose AD patients.
