Şener Begüm, Açıcı Koray, Sümer Emre
Department of Computer Engineering, Başkent University, Ankara, Turkey.
Department of Artificial Intelligence and Data Engineering, Ankara University, Ankara, Turkey.
Sci Rep. 2025 Aug 10;15(1):29260. doi: 10.1038/s41598-025-14476-0.
Alzheimer's disease is a progressive neurodegenerative disorder marked by cognitive decline, memory loss, and behavioral changes. Early diagnosis, particularly identifying Early Mild Cognitive Impairment (EMCI), is vital for managing the disease and improving patient outcomes. Detecting EMCI is challenging due to the subtle structural changes in the brain, making precise slice selection from MRI scans essential for accurate diagnosis. In this context, the careful selection of specific MRI slices that provide distinct anatomical details significantly enhances the ability to identify these early changes. The chief novelty of the study is that instead of selecting all slices, an approach for identifying the important slices is developed. The ADNI-3 dataset was used as the dataset when running the models for early detection of Alzheimer's disease. Satisfactory results have been obtained by classifying with deep learning models, vision transformers (ViT) and by adding new structures to them, together with the model proposal. In the results obtained, while an accuracy of 99.45% was achieved with EfficientNetB2 + FPN in AD vs. LMCI classification from the slices selected with SSIM, an accuracy of 99.19% was achieved in AD vs. EMCI classification, in fact, the study significantly advances early detection by demonstrating improved diagnostic accuracy of the disease at the EMCI stage. The results obtained with these methods emphasize the importance of developing deep learning models with slice selection integrated with the Vision Transformers architecture. Focusing on accurate slice selection enables early detection of Alzheimer's at the EMCI stage, allowing for timely interventions and preventive measures before the disease progresses to more advanced stages. This approach not only facilitates early and accurate diagnosis, but also lays the groundwork for timely intervention and treatment, offering hope for better patient outcomes in Alzheimer's disease. The study is finally evaluated by a statistical significance test.
阿尔茨海默病是一种进行性神经退行性疾病，其特征为认知能力下降、记忆力丧失和行为改变。早期诊断，尤其是识别早期轻度认知障碍（EMCI），对于控制该疾病和改善患者预后至关重要。由于大脑中存在细微的结构变化，检测EMCI具有挑战性，因此从MRI扫描中精确选择切片对于准确诊断至关重要。在此背景下，仔细选择能提供独特解剖细节的特定MRI切片可显著提高识别这些早期变化的能力。该研究的主要新颖之处在于，不是选择所有切片，而是开发了一种识别重要切片的方法。在运行阿尔茨海默病早期检测模型时，使用了ADNI - 3数据集。通过深度学习模型、视觉变换器（ViT）进行分类，并为其添加新结构以及模型建议，取得了令人满意的结果。在所得结果中，使用SSIM选择的切片在AD与LMCI分类中，EfficientNetB2 + FPN实现了99.45%的准确率，在AD与EMCI分类中实现了99.19%的准确率，事实上，该研究通过证明在EMCI阶段疾病诊断准确率的提高，显著推进了早期检测。用这些方法获得的结果强调了开发与视觉变换器架构集成切片选择的深度学习模型的重要性。专注于准确的切片选择能够在EMCI阶段早期检测出阿尔茨海默病，从而在疾病进展到更晚期之前进行及时干预和预防措施。这种方法不仅有助于早期准确诊断，还为及时干预和治疗奠定了基础，为改善阿尔茨海默病患者的预后带来了希望。该研究最终通过统计显著性检验进行评估。
