Nasir Rahim , Naveed Ahmad , Waseem Ullah , Jatin Bedi , Younhyun Jung
{"title":"Early progression detection from MCI to AD using multi-view MRI for enhanced assisted living","authors":"Nasir Rahim , Naveed Ahmad , Waseem Ullah , Jatin Bedi , Younhyun Jung","doi":"10.1016/j.imavis.2025.105491","DOIUrl":null,"url":null,"abstract":"<div><div>Alzheimer's disease (AD) is a progressive neurodegenerative disorder. Early detection is crucial for timely intervention and treatment to improve assisted living. Although magnetic resonance imaging (MRI) is a widely used neuroimaging modality for the diagnosis of AD, most studies focus on a single MRI plane, missing comprehensive spatial information. In this study, we proposed a novel approach that leverages multiple MRI planes (axial, coronal, and sagittal) from 3D MRI volumes to predict progression from stable mild cognitive impairment (sMCI) to progressive MCI (pMCI) and AD. We employed a list of convolutional neural networks, including EfficientNet-B7, ConvNext, and DenseNet-121, to extract deep features from each MRI plane, followed by a feature enhancement step through an attention module. The optimized feature set was then passed through a Bayesian-optimized pool of classification heads (i.e., multilayer perceptron (MLP), long short-term memory (LSTM), and multi-head attention (MHA)) to obtain the most effective model for each MRI plane. The optimal model for each MRI plane was then integrated into homogeneous and heterogeneous ensembles to further enhance the performance of the model. Using the ADNI dataset, the proposed model achieved 91% accuracy, 87% sensitivity, 88% specificity, and 92% AUC. To enhance the interpretability of the model, we used the Grad-CAM explainability technique to generate attention maps for each MRI plane, which identified critical brain regions affected by disease progression. These attention maps revealed consistent patterns of tissue damage across the MRI scans. The results demonstrate the effectiveness of combining multiplane MRI data with ensemble learning and attention mechanisms to improve the early detection and tracking of AD progression in patients with MCI, offering a more comprehensive diagnostic tool and enhanced clinical decision-making. The datasets, results, and code used to conduct the comprehensive analysis are made available to the research community through the following link: <span><span><em>https://github.com/nasir3843/Early_Progression_detection_MCI-to_AD</em></span><svg><path></path></svg></span></div></div>","PeriodicalId":50374,"journal":{"name":"Image and Vision Computing","volume":"157 ","pages":"Article 105491"},"PeriodicalIF":4.2000,"publicationDate":"2025-03-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Image and Vision Computing","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0262885625000794","RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
引用次数: 0
Abstract
Alzheimer's disease (AD) is a progressive neurodegenerative disorder. Early detection is crucial for timely intervention and treatment to improve assisted living. Although magnetic resonance imaging (MRI) is a widely used neuroimaging modality for the diagnosis of AD, most studies focus on a single MRI plane, missing comprehensive spatial information. In this study, we proposed a novel approach that leverages multiple MRI planes (axial, coronal, and sagittal) from 3D MRI volumes to predict progression from stable mild cognitive impairment (sMCI) to progressive MCI (pMCI) and AD. We employed a list of convolutional neural networks, including EfficientNet-B7, ConvNext, and DenseNet-121, to extract deep features from each MRI plane, followed by a feature enhancement step through an attention module. The optimized feature set was then passed through a Bayesian-optimized pool of classification heads (i.e., multilayer perceptron (MLP), long short-term memory (LSTM), and multi-head attention (MHA)) to obtain the most effective model for each MRI plane. The optimal model for each MRI plane was then integrated into homogeneous and heterogeneous ensembles to further enhance the performance of the model. Using the ADNI dataset, the proposed model achieved 91% accuracy, 87% sensitivity, 88% specificity, and 92% AUC. To enhance the interpretability of the model, we used the Grad-CAM explainability technique to generate attention maps for each MRI plane, which identified critical brain regions affected by disease progression. These attention maps revealed consistent patterns of tissue damage across the MRI scans. The results demonstrate the effectiveness of combining multiplane MRI data with ensemble learning and attention mechanisms to improve the early detection and tracking of AD progression in patients with MCI, offering a more comprehensive diagnostic tool and enhanced clinical decision-making. The datasets, results, and code used to conduct the comprehensive analysis are made available to the research community through the following link: https://github.com/nasir3843/Early_Progression_detection_MCI-to_AD
期刊介绍:
Image and Vision Computing has as a primary aim the provision of an effective medium of interchange for the results of high quality theoretical and applied research fundamental to all aspects of image interpretation and computer vision. The journal publishes work that proposes new image interpretation and computer vision methodology or addresses the application of such methods to real world scenes. It seeks to strengthen a deeper understanding in the discipline by encouraging the quantitative comparison and performance evaluation of the proposed methodology. The coverage includes: image interpretation, scene modelling, object recognition and tracking, shape analysis, monitoring and surveillance, active vision and robotic systems, SLAM, biologically-inspired computer vision, motion analysis, stereo vision, document image understanding, character and handwritten text recognition, face and gesture recognition, biometrics, vision-based human-computer interaction, human activity and behavior understanding, data fusion from multiple sensor inputs, image databases.