Kumar Sachin, Shastri Sourabh, Mansotra Vibhakar
Department of Computer Science and IT, University of Jammu, Jammu & Kashmir, India.
Department of Computer Science and IT, University of Jammu, Jammu & Kashmir, India.
Comput Biol Med. 2025 May;190:110029. doi: 10.1016/j.compbiomed.2025.110029. Epub 2025 Mar 18.
Neurological disorders, particularly Parkinson's Disease (PD), are serious and progressive conditions that significantly impact patients' motor functions and overall quality of life. Accurate and timely diagnosis is still crucial, but it is quite challenging. Understanding the changes in the brain linked to PD requires using neuroimaging modalities like magnetic resonance imaging (MRI). Artificial intelligence (AI), particularly deep learning (DL) methods, can potentially improve the precision of diagnosis.
In the current study, we present a novel approach that integrates T1-weighted structural MRI and rest-state functional MRI using multi-site-cum-multi-modality neuroimaging data. To maximize the richness of the data, our approach integrates deep feature-level fusion across these modalities. We proposed a custom multi-scale 2D Convolutional Neural Network (CNN) architecture that captures features at different spatial scales, enhancing the model's capacity to learn PD-related complex patterns.
With an accuracy of 97.12 %, sensitivity of 97.26 %, F1-Score of 97.63 %, Area Under the Curve (AUC) of 0.99, mean average precision (mAP) of 99.53 %, and Dice Coefficient of 0.97, the proposed Neuro_DeFused-Net diagnostic model performs exceptionally well. These results highlight the model's robust ability to distinguish PD patients from Controls (Normal), even across a variety of datasets and neuroimaging modalities.
Our findings demonstrate the transformational ability of AI-driven models to facilitate the early diagnosis of PD. The proposed Neuro_DeFused-Net model enables the rapid detection of health markers through fast analysis of complicated neuroimaging data. Thus, timely intervention and individualized treatment strategies lead to improved patient outcomes and quality of life.
神经疾病，尤其是帕金森病（PD），是严重的进行性疾病，会显著影响患者的运动功能和整体生活质量。准确及时的诊断仍然至关重要，但颇具挑战性。了解与PD相关的大脑变化需要使用磁共振成像（MRI）等神经成像技术。人工智能（AI），特别是深度学习（DL）方法，有可能提高诊断的准确性。
在本研究中，我们提出了一种新方法，该方法使用多站点兼多模态神经成像数据整合T1加权结构MRI和静息态功能MRI。为了最大限度地提高数据的丰富性，我们的方法整合了这些模态之间的深度特征级融合。我们提出了一种定制的多尺度二维卷积神经网络（CNN）架构，该架构在不同空间尺度上捕捉特征，增强了模型学习与PD相关复杂模式的能力。
所提出的Neuro_DeFused-Net诊断模型表现出色，准确率为97.12%，灵敏度为97.26%，F1分数为97.63%，曲线下面积（AUC）为0.99，平均平均精度（mAP）为99.53%，骰子系数为0.97。这些结果突出了该模型在区分PD患者和对照组（正常）方面的强大能力，即使跨越各种数据集和神经成像模态。
我们的研究结果证明了人工智能驱动模型在促进PD早期诊断方面的变革能力。所提出的Neuro_DeFused-Net模型能够通过快速分析复杂的神经成像数据快速检测健康标志物。因此，及时的干预和个性化治疗策略可改善患者的预后和生活质量。
