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Alzheimer’s disease (AD) is a progressive brain ailment that causes memory loss, cognitive decline, and behavioral changes. It is quite concerning that one in nine adults over the age of 65 have AD. Currently there is almost no cure for AD except very few experimental treatments. However, early detection offers chances to take part in clinical trials or other investigations looking at potential new and effective Alzheimer’s treatments. To detect Alzheimer’s disease, brain scans such as computed tomography (CT), magnetic resonance imaging (MRI), or positron emission tomography (PET) can be performed. Many researches have been undertaken to use computer vision on MRI images, and their accuracy ranges from 80–90%, new computer vision algorithms and cutting-edge transformers have the potential to improve this performance.We utilize advanced transformers and computer vision algorithms to enhance diagnostic accuracy, achieving an impressive 99% accuracy in categorizing Alzheimer’s disease stages through translating RNA text data and brain MRI images in near-real-time. We integrate the Local Interpretable Model-agnostic Explanations (LIME) explainable AI (XAI) technique to ensure the transformers’ acceptance, reliability, and human interpretability. LIME helps identify crucial features in RNA sequences or specific areas in MRI images essential for diagnosing AD.

阿尔茨海默病（Alzheimer’s disease, AD）是一种进行性脑部疾病，会引发记忆丧失、认知能力下降及行为改变。令人担忧的是，65岁以上的成年人中每九人就有一人罹患阿尔茨海默病。目前，除极少数试验性治疗手段外，阿尔茨海默病几乎无治愈方案。不过，早期诊断能够为患者提供参与临床试验或其他针对潜在新型有效阿尔茨海默病治疗方案的研究的机会。要检测阿尔茨海默病，可采用计算机断层扫描（computed tomography, CT）、磁共振成像（magnetic resonance imaging, MRI）以及正电子发射断层扫描（positron emission tomography, PET）等脑部影像检查手段。此前已有诸多研究将计算机视觉技术应用于MRI影像分析，其准确率介于80%至90%之间；而新型计算机视觉算法与前沿的Transformer（Transformer）模型有望进一步提升这一诊断性能。本研究采用先进的Transformer模型与计算机视觉算法以提升诊断准确率，通过近乎实时处理RNA文本数据与脑部MRI影像，实现了高达99%的阿尔茨海默病分期分类准确率。本研究集成了局部可解释模型无关解释（Local Interpretable Model-agnostic Explanations, LIME）可解释人工智能（explainable AI, XAI）技术，以保障Transformer模型的可接受性、可靠性与人类可解释性。LIME技术可帮助识别RNA序列中的关键特征，或是MRI影像中对阿尔茨海默病诊断至关重要的特定区域。