Multi-modal deep learning for predicting progression of Alzheimer's disease using bi-linear shake fusion

Goto, Tsubasa; Wang, Caihua; Li, Yuanzhong; Tsuboshita, Yukihiro

doi:10.1117/12.2549483

Cited by 2 publications

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“…For the case of using only brain images as input, our model ( M2 in Supplementary Table 2 ), based on only T1-weighted MRI images (accuracy: 78%, AUC: 85%, sensitivity: 78%, specificity: 78%), performed better than a previous deep neural network (DNN) 8 (accuracy: 75%, AUC: not available [NA], sensitivity: 75%, specificity: 75%), equivalent to a DNN 9 that used a much easier task definition (accuracy: 79%, AUC: NA, sensitivity: 75%, specificity: 82%), but was inferior to a DNN 7 using MRI and PET images (accuracy: 83%, AUC: NA, sensitivity: 80%, specificity: 84%) and a DNN 10 using mixed groups of cognitively normal (CN) + sMCI and pSMI+AD groups for training (accuracy: 83%, AUC: 88%, sensitivity: 76%, specificity: 87%). For the case of using not only images, but also non-image information, the performance of our model ( M5 in Supplementary Table 2 ) (accuracy: 88%, AUC: 95%, sensitivity: 88%, specificity: 88%) was better than that of state-of-the-art models using SVM 13 , DNN 16 , and random forest 17 (accuracy: 85%–87%, AUC: 87%–90%). Considering that the state-of-the-art methods were evaluated only by validation datasets (not test datasets), the potential superiority of our model should be greater (see Supplementary Table 6 ).…”

Section: Resultsmentioning

confidence: 84%

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A high-generalizability machine learning framework for predicting the progression of Alzheimer’s disease using limited data

et al. 2022

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Alzheimer’s disease is a neurodegenerative disease that imposes a substantial financial burden on society. A number of machine learning studies have been conducted to predict the speed of its progression, which varies widely among different individuals, for recruiting fast progressors in future clinical trials. However, because the data in this field are very limited, two problems have yet to be solved: the first is that models built on limited data tend to induce overfitting and have low generalizability, and the second is that no cross-cohort evaluations have been done. Here, to suppress the overfitting caused by limited data, we propose a hybrid machine learning framework consisting of multiple convolutional neural networks that automatically extract image features from the point of view of brain segments, which are relevant to cognitive decline according to clinical findings, and a linear support vector classifier that uses extracted image features together with non-image information to make robust final predictions. The experimental results indicate that our model achieves superior performance (accuracy: 0.88, area under the curve [AUC]: 0.95) compared with other state-of-the-art methods. Moreover, our framework demonstrates high generalizability as a result of evaluations using a completely different cohort dataset (accuracy: 0.84, AUC: 0.91) collected from a different population than that used for training.

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Section: Resultsmentioning

confidence: 84%

“…5 . For the model that uses both image and non-image information, bilinear fusion 16 was used to combine the image features extracted from images and non-image features extracted from non-image information. The same AE as shown in Supplementary Fig.…”

Section: Methodsmentioning

confidence: 99%

A high-generalizability machine learning framework for predicting the progression of Alzheimer’s disease using limited data

et al. 2022

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Exploring Deep Learning Models for Accurate Alzheimer's Disease Classification based on MRI Imaging

Rahat,

Hossain,

Ghosh

et al. 2024

EAI Endorsed Trans Perv Health Tech

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INTRODUCTION: Alzheimer's disease (AD), a complex neurodegenerative condition, presents significant challenges in early and accurate diagnosis. Early prediction of AD severity holds the potential for improved patient care and timely interventions. This research investigates the use of deep learning methodologies to forecast AD severity utilizing data extracted from Magnetic Resonance Imaging (MRI) scans. OBJECTIVES: This study aims to explore the efficacy of deep learning models in predicting the severity of Alzheimer's disease using MRI data. Traditional diagnostic methods for AD, primarily reliant on cognitive assessments, often lead to late-stage detection. MRI scans offer a non-invasive means to examine brain structure and detect pathological changes associated with AD. However, manual interpretation of these scans is labor-intensive and subject to variability. METHODS: Various deep learning models, including Convolutional Neural Networks (CNNs) and advanced architectures like DenseNet, VGG16, ResNet50, MobileNet, AlexNet, and Xception, are explored for MRI scan analysis. The performance of these models in predicting AD severity is assessed and compared. Deep learning models autonomously learn hierarchical features from the data, potentially recognizing intricate patterns associated with different AD stages that may be overlooked in manual analysis. RESULTS: The study evaluates the performance of different deep learning models in predicting AD severity using MRI scans. The results highlight the efficacy of these models in capturing subtle patterns indicative of AD progression. Moreover, the comparison underscores the strengths and limitations of each model, aiding in the selection of appropriate methodologies for AD prognosis. CONCLUSION: This research contributes to the growing field of AI-driven healthcare by showcasing the potential of deep learning in revolutionizing AD diagnosis and prognosis. The findings emphasize the importance of leveraging advanced technologies, such as deep learning, to enhance the accuracy and timeliness of AD diagnosis. However, challenges remain, including the need for large, annotated datasets, model interpretability, and integration into clinical workflows. Continued efforts in this area hold promise for improving the management of AD and ultimately enhancing patient outcomes.

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Multi-modal deep learning for predicting progression of Alzheimer's disease using bi-linear shake fusion

Cited by 2 publications

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A high-generalizability machine learning framework for predicting the progression of Alzheimer’s disease using limited data

A high-generalizability machine learning framework for predicting the progression of Alzheimer’s disease using limited data

Exploring Deep Learning Models for Accurate Alzheimer's Disease Classification based on MRI Imaging

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