AIMC Topic: Case-Control Studies

Clear Filters Showing 481 to 490 of 971 articles

Training confounder-free deep learning models for medical applications.

Nature communications
The presence of confounding effects (or biases) is one of the most critical challenges in using deep learning to advance discovery in medical imaging studies. Confounders affect the relationship between input data (e.g., brain MRIs) and output variab...

Bone Mineral Density and Content Among Patients With Coronary Artery Disease: A Comparative Study.

The American journal of the medical sciences
INTRODUCTION: Some studies indicate an association between coronary artery disease (CAD) and osteoporosis. This case-control study examined the association between body composition and bone mineral content (BMC) and density (BMD) among patients with ...

Broad Learning Enhanced H-MRS for Early Diagnosis of Neuropsychiatric Systemic Lupus Erythematosus.

Computational and mathematical methods in medicine
In this paper, we explore the potential of using the multivoxel proton magnetic resonance spectroscopy (H-MRS) to diagnose neuropsychiatric systemic lupus erythematosus (NPSLE) with the assistance of a support vector machine broad learning system (BL...

Computer-aided diagnosis of pectus excavatum using CT images and deep learning methods.

Scientific reports
Pectus excavatum (PE) is one of the most common chest wall defects. Accurate assessment of PE deformities is critical for effective surgical intervention. Index-based evaluations have become the standard for objectively estimating PE, however, these ...

Automated estimation of echocardiogram image quality in hospitalized patients.

The international journal of cardiovascular imaging
We developed a machine learning model for efficient analysis of echocardiographic image quality in hospitalized patients. This study applied a machine learning model for automated transthoracic echo (TTE) image quality scoring in three inpatient grou...

Artificial intelligence for the detection of vertebral fractures on plain spinal radiography.

Scientific reports
Vertebral fractures (VFs) cause serious problems, such as substantial functional loss and a high mortality rate, and a delayed diagnosis may further worsen the prognosis. Plain thoracolumbar radiography (PTLR) is an essential method for the evaluatio...

Neuromorphic on-chip recognition of saliva samples of COPD and healthy controls using memristive devices.

Scientific reports
Chronic Obstructive Pulmonary Disease (COPD) is a life-threatening lung disease, affecting millions of people worldwide. Implementation of Machine Learning (ML) techniques is crucial for the effective management of COPD in home-care environments. How...

Comparing machine learning with case-control models to identify confirmed dengue cases.

PLoS neglected tropical diseases
In recent decades, the global incidence of dengue has increased. Affected countries have responded with more effective surveillance strategies to detect outbreaks early, monitor the trends, and implement prevention and control measures. We have appli...

Circulating Neutrophil Extracellular Traps Signature for Identifying Organ Involvement and Response to Glucocorticoid in Adult-Onset Still's Disease: A Machine Learning Study.

Frontiers in immunology
Adult-onset Still's disease (AOSD) is an autoinflammatory disease with multisystem involvement. Early identification of patients with severe complications and those refractory to glucocorticoid is crucial to improve therapeutic strategy in AOSD. Exag...

Artificial intelligence automates and augments baseline impedance measurements from pH-impedance studies in gastroesophageal reflux disease.

Journal of gastroenterology
BACKGROUND: Artificial intelligence (AI) has potential to streamline interpretation of pH-impedance studies. In this exploratory observational cohort study, we determined feasibility of automated AI extraction of baseline impedance (AIBI) and evaluat...