Exploring Intervention Techniques for Alzheimer's Disease: Conventional Methods and the Role of AI in Advancing Care

Karthikeyan Subramanian, Faizal Hajamohideen, Vimbi Viswan, Noushath Shaffi, Mufti Mahmud
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Abstract

Alzheimer's disease (AD) is a neurodegenerative condition characterized by cognitive decline and functional impairment. This study compares conventional intervention techniques with emerging artificial intelligence (AI) approaches to AD. Intervention technique refers to a specific method or approach employed to bring about positive change in a particular situation. In the context of AD, such techniques are crucial as they aim to slow down the progression of symptoms, alleviate behavioral challenges, and support patients and their caretakers in managing the complexities of the condition. Conventional intervention techniques, such as cognitive stimulation and reality orientation, have demonstrated benefits in improving cognitive function and emotional well-being. Conventional intervention approaches are widely preferred as they have a proven track record of effectiveness, personalized response, cost-effectiveness, and patient-centered care. Despite these benefits, they are limited by individual variability in response and long-term effectiveness. On the other hand, AI-based approaches such as Computer Vision and Deep Learning (DL) hold the potential to revolutionize Alzheimer's interventions. These technologies offer early detection, personalized care, and remote monitoring capabilities. They can provide tailored interventions, assist decision-making, and enhance caregiver support. Although AI-based interventions face challenges such as data privacy and implementation complexity, their potential to transform Alzheimer's care is significant. This research paper compares conventional and AI-based approaches. It reveals that while traditional techniques are well-established and have proven benefits, AI-based interventions offer novel opportunities for personalized and advanced care. Combining the strengths of both approaches may lead to more comprehensive and effective interventions for individuals with AD. Continued research and collaboration are crucial to harness the full potential of AI in improving Alzheimer's care and enhancing the quality of life for affected individuals and their caregivers.
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探索阿尔茨海默病的干预技术:传统方法和人工智能在促进护理中的作用
阿尔茨海默病(AD)是一种神经退行性疾病,以认知能力下降和功能障碍为特征。本研究将传统的干预技术与新兴的人工智能(AI)方法进行了比较。干预技术是指在特定情况下为带来积极变化而采用的特定方法或途径。就注意力缺失症而言,这类技术至关重要,因为它们旨在减缓症状的发展、缓解行为挑战,并支持患者及其护理人员应对复杂的病情。认知刺激和现实导向等传统干预技术在改善认知功能和情绪福祉方面已取得了明显的效果。传统干预方法在有效性、个性化反应、成本效益和以患者为中心的护理方面都有良好的记录,因此受到广泛青睐。尽管有这些优点,但它们在反应和长期有效性方面受到个体差异的限制。另一方面,计算机视觉和深度学习(DL)等基于人工智能的方法有可能彻底改变阿尔茨海默氏症的干预措施。这些技术可提供早期检测、个性化护理和远程监控功能。它们可以提供量身定制的干预措施,协助决策,并加强对护理人员的支持。虽然基于人工智能的干预措施面临着数据隐私和实施复杂性等挑战,但它们改变阿尔茨海默氏症护理的潜力巨大。本研究论文比较了传统方法和基于人工智能的方法。它揭示出,虽然传统技术已得到广泛认可并具有公认的益处,但基于人工智能的干预措施为个性化和高级护理提供了新的机遇。将这两种方法的优势结合起来,可以为注意力缺失症患者提供更全面、更有效的干预。要充分发挥人工智能在改善阿尔茨海默氏症护理和提高患者及其护理人员生活质量方面的潜力,持续的研究与合作至关重要。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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