Almarri Badar
Department of Computer Science, College of Computer Science and Information Technology, King Faisal University, Al Ahsa, Saudi Arabia.
PLoS One. 2025 Aug 18;20(8):e0330085. doi: 10.1371/journal.pone.0330085. eCollection 2025.
Alzheimer's disease (AD) poses significant challenges to healthcare systems across the globe. Early and accurate AD diagnosis is crucial for effective management and treatment. Recent advances in neuroimaging and genomics provide an opportunity for developing multi-modality-based AD diagnosis models using artificial intelligence (AI) techniques. However, the data complexities cause challenges in developing interpretable AI-based AD identification models. In this study, the author built a comprehensive AD diagnostic model using magnetic resonance imaging (MRI) and gene expression data. MobileNet V3 and EfficientNet B7 model was employed to extract AD features from gene expression data. The author introduced a hybrid TWIN-Performer-based feature extraction model to derive features from MRI. The attention-based feature fusion was used to fuse the crucial features. An ensemble learning-based classification model integrating CatBoost, XGBoost, and extremely randomized tree (ERT) was developed to identify cognitively normal (CN) and AD features. The proposed model was validated on diverse datasets. It achieved a superior performance on MRI and gene expression datasets. The area under the receiver operating characteristic (AUROC) scores were consistently above 0.85, indicating excellent model performance. The use of Shapley Additive exPlanations (SHAP) values improved the model's interpretability, leading to earlier interventions and personalized treatment strategies.
阿尔茨海默病（AD）给全球医疗保健系统带来了重大挑战。早期准确的AD诊断对于有效管理和治疗至关重要。神经影像学和基因组学的最新进展为使用人工智能（AI）技术开发基于多模态的AD诊断模型提供了机会。然而，数据的复杂性给开发可解释的基于AI的AD识别模型带来了挑战。在本研究中，作者使用磁共振成像（MRI）和基因表达数据构建了一个综合的AD诊断模型。采用MobileNet V3和EfficientNet B7模型从基因表达数据中提取AD特征。作者引入了一种基于混合TWIN-Performer的特征提取模型来从MRI中提取特征。基于注意力的特征融合用于融合关键特征。开发了一种基于集成学习的分类模型，集成了CatBoost、XGBoost和极端随机树（ERT），以识别认知正常（CN）和AD特征。所提出的模型在不同数据集上进行了验证。它在MRI和基因表达数据集上取得了优异的性能。受试者操作特征曲线下面积（AUROC）分数始终高于0.85，表明模型性能优异。使用Shapley值加法解释（SHAP）提高了模型的可解释性，从而实现了早期干预和个性化治疗策略。
