Multimodal ensemble model for Alzheimer's disease conversion prediction from Early Mild Cognitive Impairment subjects

Velazquez, Matthew; Lee, Yugyung

doi:10.1016/j.compbiomed.2022.106201

Cited by 11 publications

(5 citation statements)

References 8 publications

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“…DTI has been used to study the effects of NDD on neural pathways which may lead to more reliable and early diagnosis of these diseases as well as a better understanding of how they affect the brain. In AD, ML methods were applied for defining DTI metrics ( Konukoglu et al, 2016 ; Lombardi et al, 2020 ; Xu et al, 2021 ; Agostinho et al, 2022 ) to characterize MCI ( Velazquez and Lee, 2022 ; Zhou et al, 2022a , b ; Cheng et al, 2023 ) and to predict AD early ( Savarraj et al, 2022 ). The characterization of MCI and cognitive impairment in PD ( Xu et al, 2021 ; Yang Y. et al, 2022 ; Chen B. et al, 2023 ; Huang et al, 2023 ) or the investigation of progression in PD ( Prasuhn et al, 2020 ; Yang et al, 2021a , b ) has also been addressed by the application of ML methods.…”

Section: Discussionmentioning

confidence: 99%

Toward diffusion tensor imaging as a biomarker in neurodegenerative diseases: technical considerations to optimize recordings and data processing

Müller,

Kassubek

2024

Front. Hum. Neurosci.

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Neuroimaging biomarkers have shown high potential to map the disease processes in the application to neurodegenerative diseases (NDD), e.g., diffusion tensor imaging (DTI). For DTI, the implementation of a standardized scanning and analysis cascade in clinical trials has potential to be further optimized. Over the last few years, various approaches to improve DTI applications to NDD have been developed. The core issue of this review was to address considerations and limitations of DTI in NDD: we discuss suggestions for improvements of DTI applications to NDD. Based on this technical approach, a set of recommendations was proposed for a standardized DTI scan protocol and an analysis cascade of DTI data pre-and postprocessing and statistical analysis. In summary, considering advantages and limitations of the DTI in NDD we suggest improvements for a standardized framework for a DTI-based protocol to be applied to future imaging studies in NDD, towards the goal to proceed to establish DTI as a biomarker in clinical trials in neurodegeneration.

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Section: Discussionmentioning

confidence: 99%

Toward diffusion tensor imaging as a biomarker in neurodegenerative diseases: technical considerations to optimize recordings and data processing

Müller,

Kassubek

2024

Front. Hum. Neurosci.

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“…Cognitive scores were used in 11 studies, all related to AD [ 36 , 37 , 39 , 40 , 42 , 50 , 54 , 58 , 60 , 62 , 63 ]. The most common multimodal combinations of data were cognitive scores and MRI (n = 6 studies), followed by cognitive scores plus speech data (n = 5 studies) and cognitive scores plus genetic data (n = 3 studies).…”

Section: Narrative Synthesis Of Relevant Findings From the Evidencementioning

confidence: 99%

“…Compared to traditional studies of AD and PD that rely solely on single neural imaging data or speech data, the use of genetic data in a multimodal approach has been shown to result in better classification performance. There were 5 studies using genetic data related to AD [ 33 , 40 , 52 , 56 , 63 ] and 2 to PD [ 28 , 44 ]. Genetic data were extracted as normalized numerical data from 0 to 1 indicating the risk [ 39 ] and the most common multimodal combinations were genetic data plus MRI data (n = 6 studies).…”

Section: Narrative Synthesis Of Relevant Findings From the Evidencementioning

confidence: 99%

Multimodal learning of clinically accessible tests to aid diagnosis of neurodegenerative disorders: a scoping review

Huang

Bai

et al. 2023

Health Inf Sci Syst

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With ageing populations around the world, there is a rapid rise in the number of people with Alzheimer’s disease (AD) and Parkinson’s disease (PD), the two most common types of neurodegenerative disorders. There is an urgent need to find new ways of aiding early diagnosis of these conditions. Multimodal learning of clinically accessible data is a relatively new approach that holds great potential to support early precise diagnosis. This scoping review follows the PRSIMA guidelines and we analysed 46 papers, comprising 11,750 participants, 3569 with AD, 978 with PD, and 2482 healthy controls; the recency of this topic was highlighted by nearly all papers being published in the last 5 years. It highlights the effectiveness of combining different types of data, such as brain scans, cognitive scores, speech and language, gait, hand and eye movements, and genetic assessments for the early detection of AD and PD. The review also outlines the AI methods and the model used in each study, which includes feature extraction, feature selection, feature fusion, and using multi-source discriminative features for classification. The review identifies knowledge gaps around the need to validate findings and address limitations such as small sample sizes. Applying multimodal learning of clinically accessible tests holds strong potential to aid the development of low-cost, reliable, and non-invasive methods for early detection of AD and PD.

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“…Interpretable DL models have provided efficient diagnostic and prognostic models in the DTI field [77][78][79]. For example, Vidyadharan et al calculated the four types of diffusionbased structural connectomes from a predefined atlas [78].…”

Section: Brain Diffusion Tensor Imaging (Dti)mentioning

confidence: 99%

“…They then used Grad-CAM to reveal the pattern differences between low-grade glioma and high-grade glioma patients and found distinct patterns in the frontal, temporal, and parietal lobes. Velazquez et al, applied an ensemble model of a random forest and a CNN to classify early MCI and AD, using both DTI data and clinical features as inputs [79]. They also adopted Grad-CAM as the explanation of the white matter fiber differences between early MCI and AD.…”

Section: Brain Diffusion Tensor Imaging (Dti)mentioning

confidence: 99%

Recent Advances in Explainable Artificial Intelligence for Magnetic Resonance Imaging

Qian

Wang

et al. 2023

Diagnostics

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Advances in artificial intelligence (AI), especially deep learning (DL), have facilitated magnetic resonance imaging (MRI) data analysis, enabling AI-assisted medical image diagnoses and prognoses. However, most of the DL models are considered as “black boxes”. There is an unmet need to demystify DL models so domain experts can trust these high-performance DL models. This has resulted in a sub-domain of AI research called explainable artificial intelligence (XAI). In the last decade, many experts have dedicated their efforts to developing novel XAI methods that are competent at visualizing and explaining the logic behind data-driven DL models. However, XAI techniques are still in their infancy for medical MRI image analysis. This study aims to outline the XAI applications that are able to interpret DL models for MRI data analysis. We first introduce several common MRI data modalities. Then, a brief history of DL models is discussed. Next, we highlight XAI frameworks and elaborate on the principles of multiple popular XAI methods. Moreover, studies on XAI applications in MRI image analysis are reviewed across the tissues/organs of the human body. A quantitative analysis is conducted to reveal the insights of MRI researchers on these XAI techniques. Finally, evaluations of XAI methods are discussed. This survey presents recent advances in the XAI domain for explaining the DL models that have been utilized in MRI applications.

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Multimodal ensemble model for Alzheimer's disease conversion prediction from Early Mild Cognitive Impairment subjects

Cited by 11 publications

References 8 publications

Toward diffusion tensor imaging as a biomarker in neurodegenerative diseases: technical considerations to optimize recordings and data processing

Toward diffusion tensor imaging as a biomarker in neurodegenerative diseases: technical considerations to optimize recordings and data processing

Multimodal learning of clinically accessible tests to aid diagnosis of neurodegenerative disorders: a scoping review

Recent Advances in Explainable Artificial Intelligence for Magnetic Resonance Imaging

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