Christodoulou Rafail, Christofi Giorgos, Pitsillos Rafael, Ibrahim Reina, Papageorgiou Platon, Papageorgiou Sokratis G, Vassiliou Evros, Georgiou Michalis F
Department of Radiology, Stanford University School of Medicine, Stanford, CA 94305, USA.
Faculty of Electrical Engineering, Mathematics and Computer Science, Delft University of Technology, 2628 Delft, The Netherlands.
J Clin Med. 2025 Jul 25;14(15):5261. doi: 10.3390/jcm14155261.
: Mild Cognitive Impairment (MCI) represents an intermediate stage between normal cognitive aging and Alzheimer's Disease (AD). Early and accurate identification of MCI is crucial for implementing interventions that may delay or prevent further cognitive decline. This study aims to develop a machine learning-based model for differentiating between Cognitively Normal (CN) individuals and MCI patients using data from the Alzheimer's Disease Neuroimaging Initiative (ADNI). : An ensemble classification approach was designed by integrating Extra Trees, Random Forest, and Light Gradient Boosting Machine (LightGBM) algorithms. Feature selection emphasized clinically relevant biomarkers, including Amyloid-β 42, phosphorylated tau, diastolic blood pressure, age, and gender. The dataset was split into training and held-out test sets. A probability thresholding strategy was employed to flag uncertain predictions for potential deferral, enhancing model reliability in borderline cases. : The final ensemble model achieved an accuracy of 83.2%, a recall of 80.2%, and a precision of 86.3% on the independent test set. The probability thresholding mechanism flagged 23.3% of cases as uncertain, allowing the system to abstain from low-confidence predictions. This strategy improved clinical interpretability and minimized the risk of misclassification in ambiguous cases. : The proposed AI-driven ensemble model demonstrates strong performance in classifying MCI versus CN individuals using multimodal ADNI data. Incorporating a deferral mechanism through uncertainty estimation further enhances the model's clinical utility. These findings support the integration of machine learning tools into early screening workflows for cognitive impairment.
轻度认知障碍（MCI）代表正常认知衰老与阿尔茨海默病（AD）之间的中间阶段。早期准确识别MCI对于实施可能延缓或预防进一步认知衰退的干预措施至关重要。本研究旨在利用阿尔茨海默病神经影像学倡议（ADNI）的数据开发一种基于机器学习的模型，用于区分认知正常（CN）个体和MCI患者。：通过整合极端随机树、随机森林和轻量级梯度提升机（LightGBM）算法设计了一种集成分类方法。特征选择强调临床相关生物标志物，包括淀粉样蛋白β42、磷酸化tau蛋白、舒张压、年龄和性别。数据集被分为训练集和留一测试集。采用概率阈值策略标记不确定预测以进行潜在延期，提高模型在临界情况下的可靠性。：最终的集成模型在独立测试集上的准确率达到83.2%，召回率为80.2%，精确率为86.3%。概率阈值机制将23.3%的病例标记为不确定，使系统能够避免低置信度预测。该策略提高了临床可解释性，并将模糊情况下的误分类风险降至最低。：所提出的人工智能驱动的集成模型在使用多模态ADNI数据对MCI与CN个体进行分类方面表现出强大性能。通过不确定性估计纳入延期机制进一步提高了模型的临床实用性。这些发现支持将机器学习工具集成到认知障碍的早期筛查工作流程中。
