AI Medical Compendium Journal:
Experimental neurology

Showing 1 to 10 of 11 articles

Identification of Npas4 as a biomarker for CICI by transcriptomics combined with bioinformatics and machine learning approaches.

Experimental neurology
Chemotherapy is one of the most successful strategies for treating cancer. Unfortunately, up to 70 % of cancer survivors develop cognitive impairment during or after chemotherapy, which severely affects their quality of life. We first established a m...

Transforming personalized chronic pain management with artificial intelligence: A commentary on the current landscape and future directions.

Experimental neurology
Artificial intelligence (AI) has the potential to revolutionize chronic pain management by guiding the development of effective treatment strategies that are tailored to individual patient needs. This potential comes from AI's ability to analyze larg...

A deep learning-based approach for unbiased kinematic analysis in CNS injury.

Experimental neurology
Traumatic spinal cord injury (SCI) is a devastating condition that impacts over 300,000 individuals in the US alone. Depending on the severity of the injury, SCI can lead to varying degrees of sensorimotor deficits and paralysis. Despite advances in ...

Data-driven prediction of spinal cord injury recovery: An exploration of current status and future perspectives.

Experimental neurology
Spinal Cord Injury (SCI) presents a significant challenge in rehabilitation medicine, with recovery outcomes varying widely among individuals. Machine learning (ML) is a promising approach to enhance the prediction of recovery trajectories, but its i...

Exploring new horizons: Emerging therapeutic strategies for pediatric stroke.

Experimental neurology
Pediatric stroke presents unique challenges, and optimizing treatment strategies is essential for improving outcomes in this vulnerable population. This review aims to provide an overview of new, innovative, and potential treatments for pediatric str...

Ischemic stroke-induced polyaxonal innervation at the neuromuscular junction is attenuated by robot-assisted mechanical therapy.

Experimental neurology
Ischemic stroke is a leading cause of disability world-wide. Mounting evidence supports neuromuscular pathology following stroke, yet mechanisms of dysfunction and therapeutic action remain undefined. The objectives of our study were to investigate n...

Automation of training and testing motor and related tasks in pre-clinical behavioural and rehabilitative neuroscience.

Experimental neurology
Testing and training animals in motor and related tasks is a cornerstone of pre-clinical behavioural and rehabilitative neuroscience. Yet manually testing and training animals in these tasks is time consuming and analyses are often subjective. Conseq...

Pattern classification as decision support tool in antipsychotic treatment algorithms.

Experimental neurology
Pattern classification aims to establish a new approach in personalized treatment. The scope is to tailor treatment on individual characteristics during all phases of care including prevention, diagnosis, treatment, and clinical outcome. In psychotic...

Promises and pitfalls of deep neural networks in neuroimaging-based psychiatric research.

Experimental neurology
By promising more accurate diagnostics and individual treatment recommendations, deep neural networks and in particular convolutional neural networks have advanced to a powerful tool in medical imaging. Here, we first give an introduction into method...

The opportunities and challenges of machine learning in the acute care setting for precision prevention of posttraumatic stress sequelae.

Experimental neurology
Personalized medicine is among the most exciting innovations in recent clinical research, offering the opportunity for tailored screening and management at the individual level. Biomarker-enriched clinical trials have shown increased efficiency and i...