ReviewVisual computing for industry, biomedicine, and art2020
Denouements of machine learning and multimodal diagnostic classification of Alzheimer's disease.
Review in Visual computing for industry, biomedicine, and art, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
9 citing papers in PubMed.
- Article
- A proficient approach for the classification of Alzheimer's disease using a hybridization of machine learning and deep learning.Scientific reports · 2024Article
- Revolutionizing anemia detection: integrative machine learning models and advanced attention mechanisms.Visual computing for industry, biomedicine, and art · 2024Article
- Multimodal Alzheimer's disease classification through ensemble deep random vector functional link neural network.PeerJ. Computer science · 2024Article
- Article
- Machine learning based identification of structural brain alterations underlying suicide risk in adolescents.Discover mental health · 2023Article
- Smart Earth Technologies: a pressing need for abating pollution for a better tomorrow.Environmental science and pollution research international · 2021Review
- Crime forecasting: a machine learning and computer vision approach to crime prediction and prevention.Visual computing for industry, biomedicine, and art · 2021Review
- Information Flow Pattern in Early Mild Cognitive Impairment Patients.Frontiers in neurology · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Alzheimer's disease (AD) is the most common type of dementia. The exact cause and treatment of the disease are still unknown. Different neuroimaging modalities, such as magnetic resonance imaging (MRI), positron emission tomography, and single-photon emission computed tomography, have played a significant role in the study of AD. However, the effective diagnosis of AD, as well as mild cognitive impairment (MCI), has recently drawn large attention. Various technological advancements, such as robots, global positioning system technology, sensors, and machine learning (ML) algorithms, have helped improve the diagnostic process of AD. This study aimed to determine the influence of implementing different ML classifiers in MRI and analyze the use of support vector machines with various multimodal scans for classifying patients with AD/MCI and healthy controls. Conclusions have been drawn in terms of employing different classifier techniques and presenting the optimal multimodal paradigm for the classification of AD.
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What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.