AIMC Topic: Humans

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Current methods in explainable artificial intelligence and future prospects for integrative physiology.

Pflugers Archiv : European journal of physiology
Explainable artificial intelligence (XAI) is gaining importance in physiological research, where artificial intelligence is now used as an analytical and predictive tool for many medical research questions. The primary goal of XAI is to make AI model...

Engineering a New Generation of Gene Editors: Integrating Synthetic Biology and AI Innovations.

ACS synthetic biology
CRISPR-Cas technology has revolutionized biology by enabling precise DNA and RNA edits with ease. However, significant challenges remain for translating this technology into clinical applications. Traditional protein engineering methods, such as rati...

Identifying periphery biomarkers of first-episode drug-naïve patients with schizophrenia using machine-learning-based strategies.

Progress in neuro-psychopharmacology & biological psychiatry
Schizophrenia is a complex mental disorder. Accurate diagnosis and classification of schizophrenia has always been a major challenge in clinic due to the lack of biomarkers. Therefore, identifying molecular biomarkers, particularly in the peripheral ...

Label-Free Exosomal SERS Detection Assisted by Machine Learning for Accurately Discriminating Cell Cycle Stages and Revealing the Molecular Mechanisms during the Mitotic Process.

Analytical chemistry
Cell cycle analysis is crucial for disease diagnosis and treatment, especially for investigating cell heterogeneity and regulating cell behaviors. Exosomes are highly appealing as noninvasive biomarkers for monitoring real-time changes in the cell cy...

Of Pilots and Copilots: The Evolving Role of Artificial Intelligence in Clinical Neurophysiology.

The Neurodiagnostic journal
Artificial intelligence (AI) is revolutionizing clinical neurophysiology (CNP), particularly in its applications to electroencephalography (EEG), electromyography (EMG), and polysomnography (PSG). AI enhances diagnostic accuracy and efficiency while ...

Performance of Machine Learning Models in Predicting BRAF Alterations Using Imaging Data in Low-Grade Glioma: A Systematic Review and Meta-Analysis.

World neurosurgery
BACKGROUND: Understanding the BRAF alterations preoperatively could remarkably assist in predicting tumor behavior, which leads to a more precise prognostication and management strategy. Recent advances in artificial intelligence (AI) have resulted i...

Deep Learning Approach for Automatic Heartbeat Classification.

Sensors (Basel, Switzerland)
Arrhythmia is an irregularity in the rhythm of the heartbeat, and it is the primary method for detecting cardiac abnormalities. The electrocardiogram (ECG) identifies arrhythmias and is one of the methods used to diagnose cardiac issues. Traditional ...

MRI-Based Meningioma Firmness Classification Using an Adversarial Feature Learning Approach.

Sensors (Basel, Switzerland)
The firmness of meningiomas is a critical factor that impacts the surgical approach recommended for patients. The conventional approaches that couple image processing techniques with radiologists' visual assessments of magnetic resonance imaging (MRI...

Hybrid CNN-GRU Models for Improved EEG Motor Imagery Classification.

Sensors (Basel, Switzerland)
Brain-computer interfaces (BCIs) based on electroencephalography (EEG) enable neural activity interpretation for device control, with motor imagery (MI) serving as a key paradigm for decoding imagined movements. Efficient feature extraction from raw ...