AI Medical Compendium Topic

Explore the latest research on artificial intelligence and machine learning in medicine.

Severity of Illness Index

Showing 221 to 230 of 753 articles

Clear Filters

A perspective on the evolution of semi-quantitative MRI assessment of osteoarthritis: Past, present and future.

Osteoarthritis and cartilage
OBJECTIVE: This perspective describes the evolution of semi-quantitative (SQ) magnetic resonance imaging (MRI) in characterizing structural tissue pathologies in osteoarthritis (OA) imaging research over the last 30 years.

Prediction of disease severity in COPD: a deep learning approach for anomaly-based quantitative assessment of chest CT.

European radiology
OBJECTIVES: To quantify regional manifestations related to COPD as anomalies from a modeled distribution of normal-appearing lung on chest CT using a deep learning (DL) approach, and to assess its potential to predict disease severity.

Artificial Intelligence-Based Emphysema Quantification in Routine Chest Computed Tomography: Correlation With Spirometry and Visual Emphysema Grading.

Journal of computer assisted tomography
OBJECTIVE: The aim of the study is to assess the correlation between artificial intelligence (AI)-based low attenuation volume percentage (LAV%) with forced expiratory volume in the first second to forced vital capacity (FEV1/FVC) and visual emphysem...

Predicting treatment response using machine learning: A registered report.

The British journal of clinical psychology
OBJECTIVE: Previous research on psychotherapy treatment response has mainly focused on outpatients or clinical trial data which may have low ecological validity regarding naturalistic inpatient samples. To reduce treatment failures by proactively scr...

Deep learning prediction of curve severity from rasterstereographic back images in adolescent idiopathic scoliosis.

European spine journal : official publication of the European Spine Society, the European Spinal Deformity Society, and the European Section of the Cervical Spine Research Society
PURPOSE: Radiation-free systems based on dorsal surface topography can potentially represent an alternative to radiographic examination for early screening of scoliosis, based on the ability of recognizing the presence of deformity or classifying its...

Deep learning-based lesion detection and severity grading of small-bowel Crohn's disease ulcers on double-balloon endoscopy images.

Gastrointestinal endoscopy
BACKGROUND AND AIMS: Double-balloon endoscopy (DBE) is widely used in diagnosing small-bowel Crohn's disease (CD). However, CD misdiagnosis frequently occurs if inexperienced endoscopists cannot accurately detect the lesions. The CD evaluation may al...

Machine learning models predict PTSD severity and functional impairment: A personalized medicine approach for uncovering complex associations among heterogeneous symptom profiles.

Psychological trauma : theory, research, practice and policy
OBJECTIVE: Posttraumatic stress disorder (PTSD) is a debilitating psychiatric illness, experienced by approximately 10% of the population. Heterogeneous presentations that include heightened dissociation, comorbid anxiety and depression, and emotion ...

Achieving Value by Risk Stratification With Machine Learning Model or Clinical Risk Score in Acute Upper Gastrointestinal Bleeding: A Cost Minimization Analysis.

The American journal of gastroenterology
INTRODUCTION: We estimate the economic impact of applying risk assessment tools to identify very low-risk patients with upper gastrointestinal bleeding who can be safely discharged from the emergency department using a cost minimization analysis.

Artificial intelligence quantifying endoscopic severity of ulcerative colitis in gradation scale.

Digestive endoscopy : official journal of the Japan Gastroenterological Endoscopy Society
OBJECTIVES: Existing endoscopic scores for ulcerative colitis (UC) objectively categorize disease severity based on the presence or absence of endoscopic findings; therefore, it may not reflect the range of clinical severity within each category. How...