AIMC Topic: Young Adult
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Illusory generalizability of clinical prediction models.
Science (New York, N.Y.)
Jan 11, 2024
It is widely hoped that statistical models can improve decision-making related to medical treatments. Because of the cost and scarcity of medical outcomes data, this hope is typically based on investigators observing a model's success in one or two d...
A novel artificial intelligence-assisted "vascular healing" diagnosis for prediction of future clinical relapse in patients with ulcerative colitis: a prospective cohort study (with video).
Gastrointestinal endoscopy
Jan 10, 2024
BACKGROUND AND AIMS: Image-enhanced endoscopy has attracted attention as a method for detecting inflammation and predicting outcomes in patients with ulcerative colitis (UC); however, the procedure requires specialist endoscopists. Artificial intelli...
Predicting Mood Based on the Social Context Measured Through the Experience Sampling Method, Digital Phenotyping, and Social Networks.
Administration and policy in mental health
Jan 10, 2024
Social interactions are essential for well-being. Therefore, researchers increasingly attempt to capture an individual's social context to predict well-being, including mood. Different tools are used to measure various aspects of the social context. ...
Application of Hybrid DeepLearning Architectures for Identification of Individuals with Obsessive Compulsive Disorder Based on EEG Data.
Clinical EEG and neuroscience
Jan 9, 2024
Obsessive-compulsive disorder (OCD) is a highly common psychiatric disorder. The symptoms of this condition overlap and co-occur with those of other psychiatric illnesses, making diagnosis difficult. The availability of biomarkers could be useful fo...
User confidence and electrodermal activity during haptic exploration for perceptual comparisons using a robotic system.
Assistive technology : the official journal of RESNA
Jan 8, 2024
Children with physical impairments may have trouble effectively performing the hand movements used in haptic exploration and may miss information about object properties. Assistive robotic systems with haptic feedback may enable children with physica...
Estimating vigilance from the pre-work shift sleep using an under-mattress sleep sensor.
Journal of sleep research
Jan 7, 2024
Predicting vigilance impairment in high-risk shift work occupations is critical to help to reduce workplace errors and accidents. Current methods rely on multi-night, often manually entered, sleep data. This study developed a machine learning model f...
Evaluation of Reliability of the Luna EMG Rehabilitation Robot to Assess Proprioception in the Upper Limbs in 102 Healthy Young Adults.
Medical science monitor : international medical journal of experimental and clinical research
Jan 5, 2024
BACKGROUND Proprioception, the body's ability to perceive its own position and movement, is fundamental for motor control and coordination. Reliable assessment tools are essential, particularly for conditions affecting proprioceptive function. This s...
Utilizing Artificial Intelligence-Based Deformable Registration for Global and Layer-Specific Cardiac MRI Strain Analysis in Healthy Children and Young Adults.
Academic radiology
Jan 4, 2024
RATIONALE AND OBJECTIVES: The absence of published reference values for multilayer-specific strain measurement using cardiac magnetic resonance (CMR) in young healthy individuals limits its use. This study aimed to establish normal global and layer-s...
Effect of Retrograde Autologous Priming on Coagulation Assessed by Rotation Thromboelastometry in Patients Undergoing Valvular Cardiac Surgery.
Journal of cardiothoracic and vascular anesthesia
Dec 29, 2023
OBJECTIVES: To investigate the effect of retrograde autologous priming (RAP) on coagulation function using rotation thromboelastometry (ROTEM) in patients undergoing valvular cardiac surgery.
Knowledge, attitude, and perception of Arab medical students towards artificial intelligence in medicine and radiology: A multi-national cross-sectional study.
European radiology
Dec 27, 2023
OBJECTIVES: We aimed to assess undergraduate medical students' knowledge, attitude, and perception regarding artificial intelligence (AI) in medicine.
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