目的:阿尔茨海默病(AD)早期诊断的重要性绝不是可以忽略的,因为目前还没有对其进行治疗,而不是某些治疗方法只能降低疾病进展的速度。研究差距揭示了缺乏自动非侵入性方法来诊断AD的信息,特别是在虚拟现实(VR)和人工智能的帮助下。另一个观点强调,当前的VR研究未能纳入全面的认知测试,也未能考虑老年人的设计笔记。导致不可靠的结果。
方法:本文试图设计一种适合老年人的VR环境,其中包括三种认知评估:ADAS-Cog,蒙特利尔认知评估(MoCA)和迷你精神状态考试(MMSE),已实施。此外,使用阿尔茨海默病神经成像倡议2(ADNI2)数据集,基于相应的认知测试和不同模态的磁共振成像(MRI)训练3DCNN-ML模型,并将其纳入本申请,以预测患者是否患有AD.
结果:该模型经历了三个具有不同模态的实验(认知评分(CS),MRI图像,和CS-MRI)。至于CS-MRI实验,经过训练的模型达到了97%,95%,95%,96%,精确度为94%,召回,F1分数,AUC,和精度分别。根据现有的用户体验,还使用新提出的问卷对考虑的设计笔记进行了评估,用户界面,力学,在环境援助,和VR引起的症状和影响。设计的VR系统提供了可接受的用户体验水平,参与者报告愉快和身临其境的体验。虽然有需要改进的地方,包括图形和音质,以及长时间使用HMD的舒适性问题,该系统的用户界面和机制普遍受到好评。
结论:报告的结果表明,我们的方法对3D脑体积和认知评分的综合分析能够更早地检测AD进展,可能允许及时干预和改善患者预后。所提出的集成系统为我们提供了有希望的见解,以改进使用技术的AD诊断。
OBJECTIVE: The importance of early diagnosis of Alzheimer\'s Disease (AD) is by no means negligible because no cure has been recognized for it rather than some therapies only lowering the pace of progression. The research gap reveals information on the lack of an automatic non-invasive approach toward the diagnosis of AD, in particular with the help of Virtual Reality (VR) and Artificial Intelligence. Another perspective highlights that current VR studies fail to incorporate a comprehensive range of cognitive tests and consider design notes for elderlies, leading to unreliable results.
METHODS: This paper tried to design a VR environment suitable for older adults in which three cognitive assessments namely: ADAS-Cog, Montreal Cognitive Assessment (MoCA), and Mini Mental State Exam (MMSE), are implemented. Moreover, a 3DCNN-ML model was trained based on the corresponding cognitive tests and Magnetic Resonance Imaging (MRI) with different modalities using the Alzheimer\'s Disease Neuroimaging Initiative 2 (ADNI2) dataset and incorporated into the application to predict if the patient suffers from AD.
RESULTS: The model has undergone three experiments with different modalities (Cognitive Scores (CS), MRI images, and CS-MRI). As for the CS-MRI experiment, the trained model achieved 97%, 95%, 95%, 96%, and 94% in terms of precision, recall, F1-score, AUC, and accuracy respectively. The considered design notes were also assessed using a new proposed questionnaire based on existing ones in terms of user experience, user interface, mechanics, in-env assistance, and VR induced symptoms and effects. The designed VR system provided an acceptable level of user experience, with participants reporting an enjoyable and immersive experience. While there were areas for improvement, including graphics and sound quality, as well as comfort issues with prolonged HMD use, the user interface and mechanics of the system were generally well-received.
CONCLUSIONS: The reported results state that our method\'s comprehensive analysis of 3D brain volumes and incorporation of cognitive scores enabled earlier detection of AD progression, potentially allowing for timely interventions and improved patient outcomes. The proposed integrated system provided us with promising insights for improvements in the diagnosis of AD using technologies.