基於神經心理測驗與神經網路之數位自動化阿兹海默症快篩系統

A Digital and Automatic Screening System for Alzheimer’s Disease Based on Neuropsychological Test and Neural Network

謝文婷(Wen-Ting Cheah)

指導教授 ： 傅立成

國立臺灣大學/電機資訊學院/資訊工程學研究所/碩士(2019年)

https://doi.org/10.6342/NTU201903687

摘要

阿茲海默症以及其他失智症目前不但成為全世界最嚴重的問題，也成爲全世界第五大死因。隨著人口老化，台灣社會已呈現高齡化的結構，失智症患者人數不斷地快速增加，造成家庭照顧者的負擔日益嚴峻。相較於腦影像、血液檢查等較高成本的方式，本研究基於神經心理測驗，透過分析長者所描繪的圖片並利用深度學習的方法，建立一個數位自動化阿茲海默症快篩系統。早期偵測阿茲海默症不但能夠提升他們的生活品質，也能夠減輕照顧者的壓力與照護成本。有鑒於此，本研究利用開放手繪資料集來預訓練神經網路，再將所萃取的特徵與學習到的參數進一步建立數位自動化的阿兹海默症快篩系統，輔助臨床診斷。本研究利用了118位長者紙筆描繪的複雜圖形來區分輕度認知障礙與健康長者，透過一系列的實驗進行驗證後，在ROC曲線面積的指標中達到0.913。另外，本研究也蒐集了60位長者利用數位繪圖板來描繪複雜圖形的資料，在區分阿兹海默症與健康長者實驗驗證後，在ROC曲線面積的指標中達到0.950的結果。

關鍵字

阿茲海默症 ； 輕度認知障礙 ； 快篩系統 ； 神經網絡 ； 神經心理測驗

並列摘要

Alzheimer’s disease (AD) and the other types of dementia have become one of the most serious global health issues and the fifth leading cause of death worldwide nowadays. Therefore, early detection of the disease in the stage of mild cognitive impairment (MCI), which is a prodromal stage of progressing to AD and mild AD, is crucial in order to improve the quality of life of the patients and to decrease the burden of their caregiver and clinicians. The aim of our study is to design a digital screening system based on the Rey-Osterrieth Complex Figure (ROCF) neuropsychological drawing test in order to assist the clinicians to detect whether the subject is MCI or AD against healthy control (HC) automatically. A data-driven deep learning approach is implemented in this work for building the screening system. An architecture of convolution neural network is designed for pre-training and extracting useful features from the figures drawn by the subjects. The learned features are then transferred to our collected dataset for further training of the classifier in order to distinguish the patients with MCI or AD against HC. As a result, a mean area under the receiver operating characteristic curve score (AUC) of 0.913 is achieved for classifying MCI vs. HC in traditional pencil and paper based ROCF called NTUH_ROCF dataset. On the other hand, dataset that collected using digitalize graphics tablet and smart pen based which is called NTUH_D-ROCF achieved 0.950 of AUC in classifying AD vs. HC.

並列關鍵字

Alzheimer’s Disease ； Mild cognitive impairment ； screening system ； Convolutional Neural Network ； Rey-Osterrieth Complex Figure

參考文獻

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