Since the world population is aging rapidly, the prevalence of dementia is also rising rapidly thus causing a great impact on individuals, families and societies. Accurate classification and level measurement of demen...Since the world population is aging rapidly, the prevalence of dementia is also rising rapidly thus causing a great impact on individuals, families and societies. Accurate classification and level measurement of dementia are very importance in the disease management. Numerous studies show that 18F-FDG-brain scan can differentiate various types of dementia. However, correct and accurate interpretation of nuclear images requires physicians who are well experienced. Therefore, it is worthwhile to build an automatic diagnostic system for it. In this paper, we present a novel method by using an artificial neural network (ANN) to analyze CortexID of brain PET-CT scan with clinical and laboratory data for dementia classification. Moreover, the ANN was trained to indicate the clinical severity of the disease as reflected by MMSE score. All ANNs were trained and tested again with an experienced physician’s seventy diagnosis and the results were very promising. The dementia classifier achieved 96% accuracy and the mapper network could correctly predict the MMSE score with 0.782 regression value.展开更多
文摘Since the world population is aging rapidly, the prevalence of dementia is also rising rapidly thus causing a great impact on individuals, families and societies. Accurate classification and level measurement of dementia are very importance in the disease management. Numerous studies show that 18F-FDG-brain scan can differentiate various types of dementia. However, correct and accurate interpretation of nuclear images requires physicians who are well experienced. Therefore, it is worthwhile to build an automatic diagnostic system for it. In this paper, we present a novel method by using an artificial neural network (ANN) to analyze CortexID of brain PET-CT scan with clinical and laboratory data for dementia classification. Moreover, the ANN was trained to indicate the clinical severity of the disease as reflected by MMSE score. All ANNs were trained and tested again with an experienced physician’s seventy diagnosis and the results were very promising. The dementia classifier achieved 96% accuracy and the mapper network could correctly predict the MMSE score with 0.782 regression value.
