AIMC Topic: Adult

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Teaching Motor Skills Without a Motor: A Semi-Passive Robot to Facilitate Learning.

IEEE transactions on haptics
Semi-passive rehabilitation robots resist and steer a patient's motion using only controllable passive force elements (e.g., controllable brakes). Contrarily, passive robots use uncontrollable passive force elements (e.g., springs), while active robo...

Quality assessment of expedited AI generated reformatted images for ED acquired CT abdomen and pelvis imaging.

Abdominal radiology (New York)
PURPOSE: Retrospectively compare image quality, radiologist diagnostic confidence, and time for images to reach PACS for contrast enhanced abdominopelvic CT examinations created on the scanner console by technologists versus those generated automatic...

Deep Learning-Based Denoising Enables High-Quality, Fully Diagnostic Neuroradiological Trauma CT at 25% Radiation Dose.

Academic radiology
RATIONALE AND OBJECTIVES: Traumatic neuroradiological emergencies necessitate rapid and accurate diagnosis, often relying on computed tomography (CT). However, the associated ionizing radiation poses long-term risks. Modern artificial intelligence re...

Non-traumatic brachial plexopathy identification from routine MRIs: Retrospective studies with deep learning networks.

European journal of radiology
PURPOSE: This study aims to seek an optimized deep learning model for differentiating non-traumatic brachial plexopathy from routine MRI scans.

Electrocardiogram-Based Deep Learning to Predict Mortality in Repaired Tetralogy of Fallot.

JACC. Clinical electrophysiology
BACKGROUND: Artificial intelligence-enhanced electrocardiogram (AI-ECG) analysis shows promise to predict mortality in adults with acquired cardiovascular diseases. However, its application to the growing repaired tetralogy of Fallot (rTOF) populatio...

DentAge: Deep learning for automated age prediction using panoramic dental X-ray images.

Journal of forensic sciences
Age estimation plays a crucial role in various fields, including forensic science and anthropology. This study aims to develop and validate DentAge, a deep-learning model for automated age prediction using panoramic dental X-ray images. DentAge was t...

Development and validation of a machine learning-based framework for assessing metabolic-associated fatty liver disease risk.

BMC public health
BACKGROUND: The existing predictive models for metabolic-associated fatty liver disease (MAFLD) possess certain limitations that render them unsuitable for extensive population-wide screening. This study is founded upon population health examination ...

Machine learning algorithm for predicting seizure control after temporal lobe resection using peri-ictal electroencephalography.

Scientific reports
Brain resection is curative for a subset of patients with drug resistant epilepsy but up to half will fail to achieve sustained seizure freedom in the long term. There is a critical need for accurate prediction tools to identify patients likely to ha...

Patient Perspectives on AI for Mental Health Care: Cross-Sectional Survey Study.

JMIR mental health
BACKGROUND: The application of artificial intelligence (AI) to health and health care is rapidly increasing. Several studies have assessed the attitudes of health professionals, but far fewer studies have explored the perspectives of patients or the ...

Deep humoral profiling coupled to interpretable machine learning unveils diagnostic markers and pathophysiology of schistosomiasis.

Science translational medicine
Schistosomiasis, a highly prevalent parasitic disease, affects more than 200 million people worldwide. Current diagnostics based on parasite egg detection in stool detect infection only at a late stage, and current antibody-based tests cannot disting...