ReviewInternational journal of molecular sciences2023
Diffusion Tensor Imaging in Amyotrophic Lateral Sclerosis: Machine Learning for Biomarker Development.
Review in International journal of molecular sciences, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers, 1 of them a synthesis that pooled it.
What it found
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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
19 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Performance of machine learning algorithms in diffusion tensor imaging of movement disorders: an exploratory meta-analysis.Biomedical engineering online · 2026Pooled it
- Potential Roles of Neuroimaging in Motor Neuron Disease.Current neurology and neuroscience reports · 2026Review
- Human iPSC‑based translational and reverse translational research for neurodegenerative diseases: emphasis on ALS and key advances.Japanese journal of radiology · 2026Review
- Distinguishing amyotrophic lateral sclerosis from radiculopathy using machine learning to analyze nerve conduction data.Scientific reports · 2025Article
- Review
- Blueprint of Collapse: Precision Biomarkers, Molecular Cascades, and the Engineered Decline of Fast-Progressing ALS.International journal of molecular sciences · 2025Review
- Magnetic Resonance Neuroimaging in Amyotrophic Lateral Sclerosis: A Comprehensive Umbrella Review of 18 Studies.Brain sciences · 2025Review
- Role and Potential of Artificial Intelligence in Biomarker Discovery and Development of Treatment Strategies for Amyotrophic Lateral Sclerosis.International journal of molecular sciences · 2025Review
- Molecular mechanisms and biomarkers in neurodegenerative disorders: a comprehensive review.Molecular biology reports · 2025Review
- T1-relaxation times along the corticospinal tract as a diagnostic marker in patients with amyotrophic lateral sclerosis.Frontiers in neuroimaging · 2025Article
- Involvement of the left uncinate fasciculus in the amyotrophic lateral sclerosis: an exploratory longitudinal multi-modal neuroimaging and neuropsychological study.Brain structure & function · 2024Article
- Review
- Diffusion tensor imaging biomarkers and clinical assessments in amyotrophic lateral sclerosis (ALS) patients: an exploratory study.Annals of medicine and surgery (2012) · 2024Article
- Limbic Network and Papez Circuit Involvement in ALS: Imaging and Clinical Profiles in GGGGCC Hexanucleotide Carriers inBiology · 2024Article
- Fluid biomarkers for amyotrophic lateral sclerosis: a review.Molecular neurodegeneration · 2024Review
- Exploring the Intersection of Geophysics and Diagnostic Imaging in the Health Sciences.Diagnostics (Basel, Switzerland) · 2024Review
- Toward diffusion tensor imaging as a biomarker in neurodegenerative diseases: technical considerations to optimize recordings and data processing.Frontiers in human neuroscience · 2024Review
- Amyotrophic Lateral Sclerosis as a Systemic Disease.International journal of molecular sciences · 2023Article
- Innovations In Physical Medicine and Rehabilitation: Advances in the Diagnosis, Treatment, and Care of Amyotrophic Lateral Sclerosis.Missouri medicineReview
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Diffusion tensor imaging (DTI) allows the in vivo imaging of pathological white matter alterations, either with unbiased voxel-wise or hypothesis-guided tract-based analysis. Alterations of diffusion metrics are indicative of the cerebral status of patients with amyotrophic lateral sclerosis (ALS) at the individual level. Using machine learning (ML) models to analyze complex and high-dimensional neuroimaging data sets, new opportunities for DTI-based biomarkers in ALS arise. This review aims to summarize how different ML models based on DTI parameters can be used for supervised diagnostic classifications and to provide individualized patient stratification with unsupervised approaches in ALS. To capture the whole spectrum of neuropathological signatures, DTI might be combined with additional modalities, such as structural T1w 3-D MRI in ML models. To further improve the power of ML in ALS and enable the application of deep learning models, standardized DTI protocols and multi-center collaborations are needed to validate multimodal DTI biomarkers. The application of ML models to multiparametric MRI/multimodal DTI-based data sets will enable a detailed assessment of neuropathological signatures in patients with ALS and the development of novel neuroimaging biomarkers that could be used in the clinical workup.
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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.