Neurology

Bioinformatic analysis of differentially expressed mitochondrion-related genes, immune cell infiltration, and diagnostic value in Alzheimer's disease.

BACKGROUND: Mitochondrial dysfunction and neuroinflammation are critically implicated in the pathogenesis of Alzheimer's disease (AD). However, a systematic exploration of key mitochondrion-related genes (MRGs) in AD, and their specific roles in reshaping the immune microenvironment and serving as diagnostic biomarkers, remains insufficient. METHODS: To address this, we conducted an integrative bi...

Machine learning driven modeling of synergistic perinatal risk profiles in early onset pediatric cerebral palsy.

AIM: To develop and evaluate machine learning (ML) models for early cerebral palsy (CP) prediction and identify synergistic perinatal risk factors in a pediatric population. METHOD: We conducted a retrospective case-control study using demographic, perinatal, and postnatal clinical data collected at Sidra Medicine, Qatar. Four ML models- Random Forest (RF), XGBoost, Support Vector Machine (SVM), a...

Artificial intelligence for assessment of the relationship between the mandibular canal and third molars using CBCT and panoramic radiography: a systematic review.

BACKGROUND: Accurate assessment of the relationship between mandibular third molars and the mandibular canal is essential to reduce the risk of inferi...

Technology-enabled telerehabilitation for Parkinson's disease: a scoping review of digital rehabilitation systems, delivery architectures, and implementation challenges.

INTRODUCTION: Digital technologies are increasingly integrated into neurorehabilitation programs for Parkinson's Disease (PD), enabling remote deliver...

[Artificial intelligence in neuro-ophthalmology: prospects, challenges and countermeasures].

Neuro-ophthalmic disorders feature complex etiology. Certain ocular manifestations may hint at severe neurological diseases. Current clinical practice...

Clarifying Neuroplasticity Claims in Parent-Led Early Stuttering Intervention: A Response to Onslow et al. (2026).

PURPOSE: Onslow et al. propose a simplified, parent-led early intervention for childhood stuttering based on reductions in parental speech rate and in...

Green MRI for neuroimaging: monitoring brain tumors using non-contrast sequences.

The emerging concept of "Green Radiology" aims to mitigate the environmental impact of medical imaging while maintaining high standards of patient car...

Artificial Intelligence-Based CTA Software for Real-World Detection of Large Vessel Occlusion in Acute Ischemic Stroke.

Background and purpose Artificial intelligence (AI)-based tools for CT angiography (CTA) have been introduced to support rapid detection of large vess...

Preliminary Investigation of Myoelectric Control of an Assistive Neck Exoskeleton by Individuals with Amyotrophic Lateral Sclerosis.

Neck weakness limits head control and quality of life for individuals with Amyotrophic Lateral Sclerosis (ALS). The Utah Neck Exoskeleton can restore ...

Response to the Letter to the Editor Regarding: "Predicting Postoperative Neurological Outcomes in Metastatic Spinal Tumor Surgery Using Machine Learning".

The brain structure and immunometabolic mechanisms underlying the association between frailty and dementia.

BACKGROUND: Physical frailty, characterized by heightened sensitivity to stressors and decreased physiological reserves, is an established risk factor...

Role of MRI-based synthetic CT and low-dose CT for the assessment of structural damage in axial spondyloarthritis - a review.

Structural lesions, including erosions, sclerosis, and pathological new bone formation, are key features of disease progression in axial spondyloarthr...

Novel approach to early prediction of alzheimer's disease progression using integrated deep regulatory genetic neural network and optimized deep belief networks.

UNLABELLED: To enhance the prediction progression of Alzheimer's Disease is very excavating process. PROBLEM STATEMENT: It is often very difficult to ...

An interpretable multimodal framework using compact biomarkers and Kolmogorov-Arnold networks improves the early diagnosis of Alzheimer's disease.

Early diagnosis of Alzheimer's disease (AD), especially accurate identification at the mild cognitive impairment (MCI) stage, is crucial for slowing d...

Deep learning-based hybrid analysis for axonal regeneration and myelination in rat sciatic nerve.

Quantitative histomorphometric analysis of peripheral nerves is essential for assessing axonal regeneration and remyelination, but manual analysis is ...

Detection of early-stage Parkinson's disease using wearable sensors at multiple body locations and convolutional neural networks.

Early-stage Parkinson's disease (PD) presents with subtle motor symptoms that complicate timely diagnosis. We developed a non-invasive detection frame...

Health system learning enables generalist neuroimaging models.

Frontier artificial intelligence (AI) models have advanced rapidly through training on internet-scale public data, yet such systems lack access to pri...

Artificial intelligence for detection, prediction, and severity classification of diabetic peripheral neuropathy: a systematic review.

BACKGROUND: Diabetic peripheral neuropathy (DPN) is a common complication of diabetes and an important contributor to foot ulceration and lower limb a...

Prediction model for recurrent laryngeal nerve injury in microwave ablation of thyroid nodules.

OBJECTIVE: This study aims to develop an explainable machine learning (ML) framework integrating clinical, imaging, and procedural features for predic...

The role of AI-assisted drug repurposing in neurological disorders: a systematic review of validation strategies, challenges and opportunities.

Neurological disorders refer to a diverse group of conditions that affect the brain, peripheral nerves, and spinal cord and impair socioemotional, cog...