Diagnosis of Alzheimer's disease by joining dual attention CNN and MLP based on structural MRIs, clinical and genetic data

Multimodal multiview bilinear graph convolutional network for mild cognitive impairment diagnosis

Mild cognitive impairment (MCI) is a significant predictor of the early progression of Alzheimer's disease (AD) and can serve as an important indicator of disease progression. However, many existing methods focus mainly on the image when processing brain imaging data, ignoring other non-imaging data (e.g., genetic, clinical information, etc.) that may have underlying disease information. In addition, imaging data acquired from different devices may exhibit varying degrees of heterogeneity, potentially resulting in numerous noisy connections during network construction. To address these challenges, this study proposes a Multimodal Multiview Bilinear Graph Convolution (MMBGCN) framework for disease risk prediction. Firstly, grey matter (GM), white matter (WM) and cerebrospinal fluid (CSF) features are extracted from magnetic resonance imaging (MRI), and non-imaging information is combined with the features extracted from MRI to construct a multimodal shared adjacency matrix. The shared adjacency matrix is then used to construct the multiview network so that the effect of potential disease information in the non-imaging information on the model can be considered. Finally, the MRI features extracted by the multiview network are weighted to reduce noise, and then the spatial pattern is restored by bilinear convolution. The features of the recovered spatial patterns are then combined with the genetic information for disease prediction. The proposed method is tested on the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset. Extensive experiments demonstrate the superior performance of the proposed framework and its ability to outperform other related algorithms. The average classification accuracy in the binary classification task in this study is 89.6%. The experimental results demonstrate that the method proposed in this study facilitates research on MCI diagnosis using multimodal data.

Use of Artificial Intelligence in Imaging Dementia

Alzheimer's disease is the most common cause of dementia in the elderly population (aged 65 years and over), followed by vascular dementia, Lewy body dementia, and rare types of neurodegenerative diseases, including frontotemporal dementia. There is an unmet need to improve diagnosis and prognosis for patients with dementia, as cycles of misdiagnosis and diagnostic delays are challenging scenarios in neurodegenerative diseases. Neuroimaging is routinely used in clinical practice to support the diagnosis of neurodegenerative diseases. Clinical neuroimaging is amenable to errors owing to varying human judgement as the imaging data are complex and multidimensional. Artificial intelligence algorithms (machine learning and deep learning) enable automation of neuroimaging interpretation and may reduce potential bias and ameliorate clinical decision-making. Graph convolutional network-based frameworks implicitly provide multimodal sparse interpretability to support the detection of Alzheimer's disease and its prodromal stage, mild cognitive impairment. In patients with amyloid-related imaging abnormalities, radiologists had significantly better detection performances with both ARIA-E (sensitivity higher in the assisted/deep learning method [87%] compared to unassisted [71%]) and for ARIA-H signs (sensitivity was higher in assisted [79%] compared to unassisted [69%]). A convolutional neural network method was developed, and external validation predicted final clinical diagnoses of Alzheimer's disease, dementia with Lewy bodies, mild cognitive impairment due to Alzheimer's disease, or cognitively normal with FDG-PET. The translation of artificial intelligence to clinical practice is plagued with technical, disease-related, and institutional challenges. The implementation of artificial intelligence methods in clinical practice has the potential to transform the diagnostic and treatment landscape and improve patient health and outcomes.

A novel sand cat swarm optimization algorithm-based SVM for diagnosis imaging genomics in Alzheimer's disease

In recent years, brain imaging genomics has advanced significantly in revealing underlying pathological mechanisms of Alzheimer's disease (AD) and providing early diagnosis. In this paper, we present a framework for diagnosing AD that integrates magnetic resonance imaging (fMRI) genetic preprocessing, feature selection, and a support vector machine (SVM) model. In particular, a novel sand cat swarm optimization (SCSO) algorithm, named SS-SCSO, which integrates the spiral search strategy and alert mechanism from the sparrow search algorithm, is proposed to optimize the SVM parameters. The optimization efficacy of the SS-SCSO algorithm is evaluated using CEC2017 benchmark functions, with results compared with other metaheuristic algorithms (MAs). The proposed SS-SCSO-SVM framework has been effectively employed to classify different stages of cognitive impairment in Alzheimer's Disease using imaging genetic datasets from the Alzheimer's Disease Neuroimaging Initiative. It has demonstrated excellent classification accuracies for four typical cases, including AD, early mild cognitive impairment, late mild cognitive impairment, and healthy control. Furthermore, experiment results indicate that the SS-SCSO-SVM algorithm has a stronger exploration capability for diagnosing AD compared to other well-established MAs and machine learning techniques.

Brain age prediction using combined deep convolutional neural network and multi-layer perceptron algorithms

The clinical applications of brain age prediction have expanded, particularly in anticipating the onset and prognosis of various neurodegenerative diseases. In the current study, we proposed a deep learning algorithm that leverages brain structural imaging data and enhances prediction accuracy by integrating biological sex information. Our model for brain age prediction, built on deep neural networks, employed a dataset of 3004 healthy subjects aged 18 and above. The T1-weighted images were minimally preprocessed and analyzed using the convolutional neural network (CNN) algorithm. The categorical sex information was then incorporated using the multi-layer perceptron (MLP) algorithm. We trained and validated both a CNN-only algorithm (utilizing only brain structural imaging data), and a combined CNN-MLP algorithm (using both structural brain imaging data and sex information) for age prediction. By integrating sex information with T1-weighted imaging data, our proposed CNN-MLP algorithm outperformed not only the CNN-only algorithm but also established algorithms, such as brainageR, in prediction accuracy. Notably, this hybrid CNN-MLP algorithm effectively distinguished between mild cognitive impairment and Alzheimer's disease groups by identifying variances in brain age gaps between them, highlighting the algorithm's potential for clinical application. Overall, these results underscore the enhanced precision of the CNN-MLP algorithm in brain age prediction, achieved through the integration of sex information.

A comprehensive review on federated learning based models for healthcare applications

A disease is an abnormal condition that negatively impacts the functioning of the human body. Pathology determines the causes behind the disease and identifies its development mechanism and functional consequences. Each disease has different identification methods, including X-ray scans for pneumonia, covid-19, and lung cancer, whereas biopsy and CT-scan can identify the presence of skin cancer and Alzheimer's disease, respectively. Early disease detection leads to effective treatment and avoids abiding complications. Deep learning has provided a vast number of applications in medical sectors resulting in accurate and reliable early disease predictions. These models are utilized in the healthcare industry to provide supplementary assistance to doctors in identifying the presence of diseases. Majorly, these models are trained through secondary data sources since healthcare institutions refrain from sharing patients' private data to ensure confidentiality, which limits the effectiveness of deep learning models due to the requirement of extensive datasets for training to achieve optimal results. Federated learning deals with the data in such a way that it doesn't exploit the privacy of a patient's data. In this work, a wide variety of disease detection models trained through federated learning have been rigorously reviewed. This meta-analysis provides an in-depth review of the federated learning architectures, federated learning types, hyperparameters, dataset utilization details, aggregation techniques, performance measures, and augmentation methods applied in the existing models during the development phase. The review also highlights various open challenges associated with the disease detection models trained through federated learning for future research.