AIMC Topic: Humans

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Time Efficiency and Ergonomic Assessment of a Robotic Wheelchair Transfer System.

Sensors (Basel, Switzerland)
: Caregivers experience high rates of occupational injuries, especially during wheelchair transfers, which often result in back pain and musculoskeletal disorders due to the physical demands of lifting and repositioning. While mechanical floor lifts,...

Use of Artificial Intelligence in Imaging Dementia.

Cells
Alzheimer's disease is the most common cause of dementia in the elderly population (aged 65 years and over), followed by vascular dementia, Lewy body dementia, and rare types of neurodegenerative diseases, including frontotemporal dementia. There is ...

Identification of a machine learning-based diagnostic model for axial spondyloarthritis in rheumatological routine care using a random forest approach.

RMD open
OBJECTIVES: In axial spondyloarthritis (axSpA), early diagnosis is crucial, but diagnostic delay remains long and diagnostic criteria do not exist. We aimed to identify a diagnostic model that distinguishes patients with axSpA from patients without a...

Development and validation of a rheumatoid arthritis case definition: a machine learning approach using data from primary care electronic medical records.

BMC medical informatics and decision making
BACKGROUND: Rheumatoid Arthritis (RA) is a chronic inflammatory disease that is primarily diagnosed and managed by rheumatologists; however, it is often primary care providers who first encounter RA-related symptoms. This study developed and validate...

Machine learning predicts pulmonary Long Covid sequelae using clinical data.

BMC medical informatics and decision making
Long COVID is a multi-systemic disease characterized by the persistence or occurrence of many symptoms that in many cases affect the pulmonary system. These, in turn, may deteriorate the patient's quality of life making it easier to develop severe co...

Regulating professional ethics in a context of technological change.

BMC medical ethics
BACKGROUND: Technological change is impacting the work of health professionals, especially with recent developments in artificial intelligence. Research has raised many ethical considerations respecting clinical applications of artificial intelligenc...

F-FDG PET/CT-based habitat radiomics combining stacking ensemble learning for predicting prognosis in hepatocellular carcinoma: a multi-center study.

BMC cancer
BACKGROUND: This study aims to develop habitat radiomic models to predict overall survival (OS) for hepatocellular carcinoma (HCC), based on the characterization of the intratumoral heterogeneity reflected in F-FDG PET/CT images.

MRI-based radiomic and machine learning for prediction of lymphovascular invasion status in breast cancer.

BMC medical imaging
OBJECTIVE: Lymphovascular invasion (LVI) is critical for the effective treatment and prognosis of breast cancer (BC). This study aimed to investigate the value of eight machine learning models based on MRI radiomic features for the preoperative predi...

Enhanced prediction of hemolytic activity in antimicrobial peptides using deep learning-based sequence analysis.

BMC bioinformatics
Antimicrobial peptides (AMPs) are a promising class of antimicrobial drugs due to their broad-spectrum activity against microorganisms. However, their clinical application is limited by their potential to cause hemolysis, the destruction of red blood...

A lightweight deep learning method to identify different types of cervical cancer.

Scientific reports
Cervical cancer is the second most common cancer in women's bodies after breast cancer. Cervical cancer develops from dysplasia or cervical intraepithelial neoplasm (CIN), the early stage of the disease, and is characterized by the aberrant growth of...