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Wrist and elbow fracture detection and segmentation by artificial intelligence using point-of-care ultrasound.

Journal of ultrasound
PURPOSE: Distal radius (wrist) and supracondylar (elbow) fractures are common in children presenting to Pediatric Emergency Departments (EDs). These fractures are treated conservatively or surgically depending on deformity severity. Radiographs are t...

Advances in research on receptor heterogeneity in breast cancer liver metastasis.

Bioscience trends
Breast cancer liver metastasis (BCLM) presents a critical challenge in breast cancer treatment and has substantial epidemiological and clinical significance. Receptor status is pivotal in managing both primary breast cancer and its liver metastases. ...

Investigating perioperative pressure injuries and factors influencing them with imbalanced samples using a Synthetic Minority Over-sampling Technique.

Bioscience trends
This study investigates the use of machine learning (ML) models combined with a Synthetic Minority Over-sampling Technique (SMOTE) and its variants to predict perioperative pressure injuries (PIs) in an imbalanced dataset. PIs are a significant healt...

Sex classification accuracy through machine learning algorithms - morphometric variables of human ear and nose.

BMC research notes
OBJECTIVE: Sex determination is an important parameter for personal identification in forensic and medico-legal examinations. The study aims at predicting sex accuracy from different parameters of ear and nose by using a novel approach of Machine Lea...

Prediction of postoperative intensive care unit admission with artificial intelligence models in non-small cell lung carcinoma.

European journal of medical research
BACKGROUND: There is no standard practice for intensive care admission after non-small cell lung cancer surgery. In this study, we aimed to determine the need for intensive care admission after non-small cell lung cancer surgery with deep learning mo...

CRISP: A causal relationships-guided deep learning framework for advanced ICU mortality prediction.

BMC medical informatics and decision making
BACKGROUND: Mortality prediction is critical in clinical care, particularly in intensive care units (ICUs), where early identification of high-risk patients can inform treatment decisions. While deep learning (DL) models have demonstrated significant...

Unraveling relevant cross-waves pattern drifts in patient-hospital risk factors among hospitalized COVID-19 patients using explainable machine learning methods.

BMC infectious diseases
BACKGROUND: Several studies explored factors related to adverse clinical outcomes among COVID-19 patients but lacked analysis of the impact of the temporal data shifts on the strength of association between different predictors and adverse outcomes. ...

A prediction model of pediatric bone density from plain spine radiographs using deep learning.

Scientific reports
Osteoporosis, a bone disease characterized by decreased bone mineral density (BMD) resulting in decreased mechanical strength and an increased fracture risk, remains poorly understood in children. Herein, we developed/validated a deep learning-based ...

A deep learning approach for blood glucose monitoring and hypoglycemia prediction in glycogen storage disease.

Scientific reports
Glycogen storage disease (GSD) is a group of rare inherited metabolic disorders characterized by abnormal glycogen storage and breakdown. These disorders are caused by mutations in G6PC1, which is essential for proper glucose storage and metabolism. ...

Automatic development of speech-in-noise hearing tests using machine learning.

Scientific reports
Understanding speech in noisy environments is a primary challenge for individuals with hearing loss, affecting daily communication and quality of life. Traditional speech-in-noise tests are essential for screening and diagnosing hearing loss but are ...