RESUMO
BACKGROUND: Alzheimer disease (AD) and other types of dementia are now considered one of the world's most pressing health problems for aging people worldwide. It was the seventh-leading cause of death, globally, in 2019. With a growing number of patients with dementia and increasing costs for treatment and care, early detection of the disease at the stage of mild cognitive impairment (MCI) will prevent the rapid progression of dementia. In addition to reducing the physical and psychological stress of patients' caregivers in the long term, it will also improve the everyday quality of life of patients. OBJECTIVE: The aim of this study was to design a digital screening system to discriminate between patients with MCI and AD and healthy controls (HCs), based on the Rey-Osterrieth Complex Figure (ROCF) neuropsychological test. METHODS: The study took place at National Taiwan University between 2018 and 2019. In order to develop the system, pretraining was performed using, and features were extracted from, an open sketch data set using a data-driven deep learning approach through a convolutional neural network. Later, the learned features were transferred to our collected data set to further train the classifier. The first data set was collected using pen and paper for the traditional method. The second data set used a tablet and smart pen for data collection. The system's performance was then evaluated using the data sets. RESULTS: The performance of the designed system when using the data set that was collected using the traditional pen and paper method resulted in a mean area under the receiver operating characteristic curve (AUROC) of 0.913 (SD 0.004) when distinguishing between patients with MCI and HCs. On the other hand, when discriminating between patients with AD and HCs, the mean AUROC was 0.950 (SD 0.003) when using the data set that was collected using the digitalized method. CONCLUSIONS: The automatic ROCF test scoring system that we designed showed satisfying results for differentiating between patients with AD and MCI and HCs. Comparatively, our proposed network architecture provided better performance than our previous work, which did not include data augmentation and dropout techniques. In addition, it also performed better than other existing network architectures, such as AlexNet and Sketch-a-Net, with transfer learning techniques. The proposed system can be incorporated with other tests to assist clinicians in the early diagnosis of AD and to reduce the physical and mental burden on patients' family and friends.
RESUMO
Alzheimer disease and other dementias have become the 7th cause of death worldwide. Still lacking a cure, an early detection of the disease in order to provide the best intervention is crucial. To develop an assessment system for the general public, speech analysis is the optimal solution since it reflects the speaker's cognitive skills abundantly and data collection is relatively inexpensive compared with brain imaging, blood testing, etc. While most of the existing literature extracted statistics-based features and relied on a feature selection process, we have proposed a novel Feature Sequence representation and utilized a data-driven approach, namely, the recurrent neural network to perform classification in this study. The system is also shown to be fully-automated, which implies the system can be deployed widely to all places easily. To validate our study, a series of experiments have been conducted with 120 speech samples, and the score in terms of the area under the receiver operating characteristic curve is as high as 0.838.
