School of Engineering, King Mongkut's Institute of Technology Ladkrabang, Bangkok 10520, Thailand.
Department of Ophthalmology, Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok 10700, Thailand.
Sensors (Basel). 2024 Aug 11;24(16):5192. doi: 10.3390/s24165192.
Alzheimer's disease is a type of neurodegenerative disorder that is characterized by the progressive degeneration of brain cells, leading to cognitive decline and memory loss. It is the most common cause of dementia and affects millions of people worldwide. While there is currently no cure for Alzheimer's disease, early detection and treatment can help to slow the progression of symptoms and improve quality of life. This research presents a diagnostic tool for classifying mild cognitive impairment and Alzheimer's diseases using feature-based machine learning applied to optical coherence tomographic angiography images (OCT-A). Several features are extracted from the OCT-A image, including vessel density in five sectors, the area of the foveal avascular zone, retinal thickness, and novel features based on the histogram of the range-filtered OCT-A image. To ensure effectiveness for a diverse population, a large local database for our study was collected. The promising results of our study, with the best accuracy of 92.17,% will provide an efficient diagnostic tool for early detection of Alzheimer's disease.
阿尔茨海默病是一种神经退行性疾病，其特征是脑细胞逐渐退化，导致认知能力下降和记忆力丧失。它是痴呆症最常见的原因，影响着全球数百万人。虽然目前尚无治愈阿尔茨海默病的方法，但早期发现和治疗可以帮助减缓症状的进展并提高生活质量。本研究提出了一种使用基于特征的机器学习对光学相干断层扫描血管造影图像（OCT-A）进行分类的轻度认知障碍和阿尔茨海默病的诊断工具。从 OCT-A 图像中提取了几个特征，包括五个区域的血管密度、中心凹无血管区的面积、视网膜厚度以及基于范围滤波 OCT-A 图像的直方图的新特征。为了确保对不同人群的有效性，我们的研究收集了一个大型的本地数据库。我们的研究结果很有前景，准确率高达 92.17%，将为早期发现阿尔茨海默病提供一种有效的诊断工具。
