ReviewMolecular imaging
Advancements in Imaging Technologies and AI Integration for Neurodegenerative Disease Management: A Narrative Review.
Review in Molecular imaging. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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.
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Who cites it
13 citing papers in PubMed.
- Qualitative and quantitative assessment of intratumoral fat using chemical-shift MRI for predicting histological grade of hepatocellular carcinoma.BMC medical imaging · 2026Article
- Radiomics-based unsupervised clustering and deep learning nomogram for response prediction after neoadjuvant chemotherapy in locally advanced laryngeal cancer.BMC medical imaging · 2026Article
- CT-Based Liver Segmentation for Liver Surgery: A Hybrid Approach Based on 3D U-Net-ELM Model.Biomedicines · 2026Article
- Independent component analysis of brain network alterations associated with cognitive impairment in coal workers' pneumoconiosis.BMC medical imaging · 2026Article
- Automatic identification of different stage of Alzheimer's disease using multimodal MRI and artificial intelligence.BMC medical imaging · 2026Article
- Leveraging infarct topography for early warning: a robust model for predicting malignant cerebral edema after endovascular treatment in acute ischemic stroke.BMC medical imaging · 2026Article
- Machine-Learning Based Prognostic Model for Predicting Early Recurrence in HCC Patients After Hepatectomies: An Explainable AI Approach.Journal of hepatocellular carcinoma · 2026Article
- Discrimination of Triple-Negative Breast Cancer: A Robust Clinical Baseline versus Multimodal Magnetic Resonance Imaging Integrated Models with Assessment of Generalizability.Breast cancer (Dove Medical Press) · 2026Article
- Lipid dysregulation as a convergent pathway linking environmental exposures to stroke.Frontiers in aging neuroscience · 2026Review
- AI-assisted forecasting in microsurgery: A dual-component framework for global publication trends.PloS one · 2026Article
- Diagnostic value of whole-tumor ADC histogram parameters combined with ISUP grade and MRI-EPE score for predicting extracapsular extension in PI-RADS 4-5 prostate cancer.Frontiers in oncology · 2026Article
- An Interpretable Machine Learning Model for Predicting in-Hospital Progression in Initially Mild Hypertriglyceridemia-Induced Acute Pancreatitis Using Clinical and Non-Contrast CT Features.International journal of general medicine · 2026Article
- Decoding Intratumoral Heterogeneity in Breast Cancer: The Evolution From Radiomics to Topological Quantification for Predicting Lymphovascular Invasion.Cancer control : journal of the Moffitt Cancer CenterReview
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Neurodegenerative diseases, characterized by progressive neuronal degeneration, are increasingly prevalent due to global aging trends and impose a significant burden on patients. No cure currently exists, with oxidative stress and inflammation serving as key drivers of disease progression. Advances in imaging technologies and artificial intelligence (AI) offer new opportunities for early diagnosis, monitoring, and treatment evaluation. This review aims to summarize the role of advanced neuroimaging modalities and AI integration in improving the diagnosis, monitoring, and management of neurodegenerative diseases, while highlighting current challenges and future directions. Material and Methods: A narrative review was conducted based on published literature on neuroimaging techniques in neurodegenerative diseases. Key modalities included structural and functional magnetic resonance imaging (MRI, fMRI), diffusion tensor imaging (DTI), positron emission tomography (PET), and single-photon emission computed tomography (SPECT). The integration of AI in image analysis was evaluated for its impact on diagnostic accuracy and workflow efficiency. Sources were selected from peer-reviewed journals focusing on clinical applications, technical advancements, and multimodal imaging strategies. Results Structural MRI, fMRI, and DTI provide detailed insights into brain atrophy and microstructural integrity, while PET and SPECT enable molecular-level assessment of metabolism and pathology. AI-enhanced analysis reduces interpretation variability and improves diagnostic precision. Despite these advances, high costs, limited accessibility, and inter-expert subjectivity remain major barriers. Emerging multimodal approaches and AI-driven tools show promise in enabling earlier detection and personalized treatment monitoring. Conclusion: The integration of advanced imaging and AI holds transformative potential for neurodegenerative disease management. Future efforts should prioritize cost reduction, improved accessibility, and seamless multimodal data fusion to translate these technologies into routine clinical practice.
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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.