隨著人口老化，阿茲海默症成為最常見的失智症類型，嚴重影響患者生活品質。 本研究旨在探討結合腦室體積與認知評估量表的診斷方法，以期在阿茲海默症的診斷 中取得優異表現。首先對3D磁振造影（MRI）影像進行資料預處理，計算每張切片中 的腦室面積，最終透過切片堆疊的方式加總成腦室體積。接著，將腦室體積及認知評 估量表數據輸入深度學習中的全連接層神經網絡，區分認知正常對照組（CN）、輕度認 知障礙患者（MCI）和阿茲海默症患者（AD）。結果顯示，三元分類準確率為0.87，認 知正常與輕度認知障礙分類準確率為0.95，輕度認知障礙與阿茲海默症分類準確率為 0.84，認知正常與阿茲海默症分類準確率為0.99。研究結果表明，腦室體積擴大與認知 功能下降具有相關性，為阿茲海默症的早期診斷和進展監測提供了新的思路。然而， 本研究在區分輕度認知障礙與阿茲海默症方面仍有進步空間，未來可增加與阿茲海默 症高度相關的特徵，以改進診斷方法的準確性。總結來說，本研究提出的綜合診斷方 法在提高阿茲海默症診斷準確性方面具有潛力，為未來相關研究和臨床應用提供了重 要參考。;With the aging population, Alzheimer’s disease has become the most common type of de mentia, severely affecting the quality of life of patients. This study aims to explore a diagnostic method that combines ventricular volume and cognitive assessment scales to achieve superior performance in diagnosing Alzheimer’s disease. First, we preprocessed the data from 3D mag netic resonance imaging (MRI) to calculate the ventricular area in each slice, and then summed these areas through slice stacking to obtain the total ventricular volume. Next, we input the ventricular volume and cognitive assessment scale data into a fully connected layer neural net work in deep learning to distinguish between cognitively normal controls (CN), mild cognitive impairment patients (MCI), and Alzheimer’s disease patients (AD). The results showed that the accuracy of the ternary classification is 0.87, the accuracy of distinguishing between cognitively normal and mild cognitive impairment is 0.95, the accu racy of distinguishing between mild cognitive impairment and Alzheimer’s disease is 0.84, and the accuracy of distinguishing between cognitively normal and Alzheimer’s disease is 0.99. The results indicate that there is a correlation between increased ventricular volume and cognitive de cline, providing new insights for the early diagnosis and progression monitoring of Alzheimer’s disease. However, there is still room for improvement in distinguishing between mild cognitive im pairment and Alzheimer’s disease. Future work could focus on incorporating features highly correlated with Alzheimer’s disease to improve the accuracy of the diagnostic method. In con clusion, the comprehensive diagnostic method proposed in this study has potential in improving the accuracy of Alzheimer’s disease diagnosis, providing an important reference for future re search and clinical applications.
