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Recognizing artery segments on carotid ultrasonography using embedding concatenation of deep image and vision-language models.

Physics in medicine and biology
Evaluating large artery atherosclerosis is critical for predicting and preventing ischemic strokes. Ultrasonographic assessment of the carotid arteries is the preferred first-line examination due to its ease of use, noninvasive, and absence of radiat...

Evaluating machine- and deep learning approaches for artifact detection in infant EEG: classifier performance, certainty, and training size effects.

Biomedical physics & engineering express
Electroencephalography (EEG) is essential for studying infant brain activity but is highly susceptible to artifacts due to infants' movements and physiological variability. Manual artifact detection is labor-intensive and subjective, underscoring the...

Diagnostic value of small dense low-density lipoprotein and trace elements in coronary artery disease.

Annales de biologie clinique
Coronary artery disease (CAD) is a worldwide leading cause of death. Considering that 20%-40% of patients with CAD have a long asymptomatic period of atherosclerosis, it has become urgent to explore the feasibility of diagnosing CAD at an early stage...

Deep learning-based model for difficult transfemoral access prediction compared with human assessment in stroke thrombectomy.

Journal of neurointerventional surgery
BACKGROUND: In mechanical thrombectomy (MT), extracranial vascular tortuosity is among the main determinants of procedure duration and success. Currently, no rapid and reliable method exists to identify the anatomical features precluding fast and sta...

Serum calcium-based interpretable machine learning model for predicting anastomotic leakage after rectal cancer resection: A multi-center study.

World journal of gastroenterology
BACKGROUND: Despite the promising prospects of utilizing artificial intelligence and machine learning (ML) for comprehensive disease analysis, few models constructed have been applied in clinical practice due to their complexity and the lack of reaso...

Update on the detection of frailty in older adults: a multicenter cohort machine learning-based study protocol.

Aging
BACKGROUND: This study aims to investigate the relationship between muscle activation variables assessed via ultrasound and the comprehensive assessment of geriatric patients, as well as to analyze ultrasound images to determine their correlation wit...

Diagnostic performance of actigraphy in Alzheimer's disease using a machine learning classifier - a cross-sectional memory clinic study.

Alzheimer's research & therapy
BACKGROUND: Movement patterns, activity levels and circadian rhythm are altered in Alzheimer's disease (AD) and can be assessed by actigraphy using wearable sensors. We aimed to determine the diagnostic performance of actigraphy in AD in a memory cli...

Early prediction of bone destruction in rheumatoid arthritis through machine learning analysis of plasma metabolites.

Arthritis research & therapy
BACKGROUND: To develop a predictive model for bone destruction in patients with rheumatoid arthritis (RA), based on the characteristics of plasma metabolites and common clinical indicators.

Knowledge, perception, and attitude of Egyptian dental students toward the role of robotics and artificial intelligence in dental practices - a cross-sectional study.

BMC oral health
BACKGROUND: Rapid technological progress has made robotics (R) and artificial intelligence (AI) essential components of our everyday existence. In addition, robots designed for dental applications have been created. This study aimed to assess Egyptia...

A machine learning based prediction model for short term efficacy of nasopharyngeal carcinoma.

Scientific reports
The radiological dosimetric parameters and clinical features were screened by machine learning to construct a prediction model for the short-term efficacy of locally advanced Nasopharyngeal Carcinoma (LANPC). Patients diagnosed with Nasopharyngeal Ca...