Predicting Alzheimer's disease progression using deep recurrent neural networks

Nguyen, Minh; He, Tong; An, Lijun; Alexander, Daniel C.; Feng, Jiashi; Yeo, B. T. Thomas; Initiative, Alzheimer’s Disease Neuroimaging

doi:10.1016/j.neuroimage.2020.117203

Cited by 92 publications

(52 citation statements)

References 64 publications

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“…Linguistic analysis may characterize cognitive impairments or Alzheimer's disease, which are manifested by decreased lexical diversity and grammatical complexity, loss of semantic skills, word finding difficulties and frequent use of filler sounds (Nguyen et al 2020;Robin et al 2020). Because linguistic disabilities have different reasons (Antaki & Wilkinson 2012) and different effects (traumata, autism, dementia and others), different types of technology are needed to support speakers suffering from those disorders, and speakers who communicate with people with interactional disabilities.…”

Section: Health and Carementioning

confidence: 99%

The Dawn of the Human-Machine Era: A forecast of new and emerging language technologies

Sayers¹,

Sousa‐Silva²,

Höhn³

et al. 2021

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New language technologies are coming, thanks to the huge and competing private investment fuelling rapid progress; we can either understand and foresee their effects, or be taken by surprise and spend our time trying to catch up. This report scketches out some transformative new technologies that are likely to fundamentally change our use of language. Some of these may feel unrealistically futuristic or far-fetched, but a central purpose of this report - and the wider LITHME network - is to illustrate that these are mostly just the logical development and maturation of technologies currently in prototype. But will everyone benefit from all these shiny new gadgets? Throughout this report we emphasise a range of groups who will be disadvantaged and issues of inequality. Important issues of security and privacy will accompany new language technologies. A further caution is to re-emphasise the current limitations of AI. Looking ahead, we see many intriguing opportunities and new capabilities, but a range of other uncertainties and inequalities. New devices will enable new ways to talk, to translate, to remember, and to learn. But advances in technology will reproduce existing inequalities among those who cannot afford these devices, among the world’s smaller languages, and especially for sign language. Debates over privacy and security will flare and crackle with every new immersive gadget. We will move together into this curious new world with a mix of excitement and apprehension - reacting, debating, sharing and disagreeing as we always do. Plug in, as the human-machine era dawns.

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Section: Health and Carementioning

confidence: 99%

The Dawn of the Human-Machine Era: A forecast of new and emerging language technologies

Sayers¹,

Sousa‐Silva²,

Höhn³

et al. 2021

View full text Add to dashboard Cite

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“…Predictive models were built on two different sets of features. The first set of features (all features) was preselected following [ 48 ] and expanded to include key features from the ADNI TadPole competition [ 49 ] in addition to a few features that were available for over 90% of the ADNI cohort. This resulted in a set of 37 features including biomarkers tau, p-tau, and A β 42 in CSF, the PET measures of AV45 and FDG, seven different size measurements of brain regions, and 15 different cognitive tests.…”

Section: Methodsmentioning

confidence: 99%

Predicting progression and cognitive decline in amyloid-positive patients with Alzheimer’s disease

et al. 2021

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Background In Alzheimer’s disease, amyloid- β (A β) peptides aggregate in the lowering CSF amyloid levels - a key pathological hallmark of the disease. However, lowered CSF amyloid levels may also be present in cognitively unimpaired elderly individuals. Therefore, it is of great value to explain the variance in disease progression among patients with A β pathology. Methods A cohort of n=2293 participants, of whom n=749 were A β positive, was selected from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database to study heterogeneity in disease progression for individuals with A β pathology. The analysis used baseline clinical variables including demographics, genetic markers, and neuropsychological data to predict how the cognitive ability and AD diagnosis of subjects progressed using statistical models and machine learning. Due to the relatively low prevalence of A β pathology, models fit only to A β-positive subjects were compared to models fit to an extended cohort including subjects without established A β pathology, adjusting for covariate differences between the cohorts. Results A β pathology status was determined based on the A β42/A β40 ratio. The best predictive model of change in cognitive test scores for A β-positive subjects at the 2-year follow-up achieved an R2 score of 0.388 while the best model predicting adverse changes in diagnosis achieved a weighted F1 score of 0.791. A β-positive subjects declined faster on average than those without A β pathology, but the specific level of CSF A β was not predictive of progression rate. When predicting cognitive score change 4 years after baseline, the best model achieved an R2 score of 0.325 and it was found that fitting models to the extended cohort improved performance. Moreover, using all clinical variables outperformed the best model based only on a suite of cognitive test scores which achieved an R2 score of 0.228. Conclusion Our analysis shows that CSF levels of A β are not strong predictors of the rate of cognitive decline in A β-positive subjects when adjusting for other variables. Baseline assessments of cognitive function accounts for the majority of variance explained in the prediction of 2-year decline but is insufficient for achieving optimal results in longer-term predictions. Predicting changes both in cognitive test scores and in diagnosis provides multiple perspectives of the progression of potential AD subjects.

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“…In [92], a multi-modal process was proposed, where automatic segmentation of hippocampal was performed for the classification of AD. A minimal RNN model to predict longitudinal AD dementia progression was proposed in [93] using 1677 participants. It was identified that the proposed model achieved better classification performance as compared to the baseline algorithms.…”

Section: ) Dl-based Approaches In Ad Diagnosismentioning

confidence: 99%

Machine Learning and Deep Learning Approaches for Brain Disease Diagnosis: Principles and Recent Advances

et al. 2021

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Brain is the controlling center of our body. With the advent of time, newer and newer brain diseases are being discovered. Thus, because of the variability of brain diseases, existing diagnosis or detection systems are becoming challenging and are still an open problem for research. Detection of brain diseases at an early stage can make a huge difference in attempting to cure it. In recent years, the use of artificial intelligence (AI) is surging through all spheres of science, and no doubt, it is revolutionizing the field of neurology. Application of AI in medical science has made brain disease prediction and detection more accurate and precise. In this study, we present a review on recent machine learning and deep learning approaches in detecting four brain diseases such as Alzheimer's disease (AD), brain tumor, epilepsy, and Parkinson's disease. 147 recent articles on four brain diseases are reviewed considering diverse machine learning and deep learning approaches, modalities, datasets etc. Twenty-two datasets are discussed which are used most frequently in the reviewed articles as a primary source of brain disease data. Moreover, a brief overview of different feature extraction techniques that are used in diagnosing brain diseases is provided. Finally, key findings from the reviewed articles are summarized and a number of major issues related to machine learning/deep learning-based brain disease diagnostic approaches are discussed. Through this study, we aim at finding the most accurate technique for detecting different brain diseases which can be employed for future betterment. INDEX TERMS Alzheimer's disease, brain tumor, deep learning, epilepsy, Parkinson's disease, machine learning NOMENCLATURE The most commonly used abbreviations in this survey are summarized in Table 1.

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Predicting Alzheimer's disease progression using deep recurrent neural networks

Cited by 92 publications

References 64 publications

The Dawn of the Human-Machine Era: A forecast of new and emerging language technologies

The Dawn of the Human-Machine Era: A forecast of new and emerging language technologies

Predicting progression and cognitive decline in amyloid-positive patients with Alzheimer’s disease

Machine Learning and Deep Learning Approaches for Brain Disease Diagnosis: Principles and Recent Advances

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