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Unlocking insights: Using machine learning to identify wasting and risk factors in Egyptian children under 5.

Nutrition (Burbank, Los Angeles County, Calif.)
INTRODUCTION: Malnutrition, particularly wasting, continues to be a significant public health issue among children under five years in Egypt. Despite global advancements in child health, the prevalence of wasting remains a critical concern. This stud...

Pterygoid implant-based maxillary full-arch rehabilitation using an autonomous robot system: A case report.

Journal of prosthodontics : official journal of the American College of Prosthodontists
Pterygoid implant placement has been proven to be a viable option in full-arch implant rehabilitation for extremely atrophic maxillae. Nevertheless, the utilization of pterygoid implants remains a challenge for the dentist due to the difficulties of ...

Data-driven explainable machine learning for personalized risk classification of myasthenic crisis.

International journal of medical informatics
OBJECTIVE: Myasthenic crisis (MC) is a critical progression of Myasthenia gravis (MG), requiring intensive care treatment and invasive therapies. Classifying patients at high-risk for MC facilitates treatment decisions such as changes in medication o...

Development of a Deep Learning Model for Classification of Hepatic Steatosis from Clinical Standard Ultrasound.

Ultrasound in medicine & biology
OBJECTIVE: Early detection and monitoring of hepatic steatosis can help establish appropriate preventative measures against progression to more advanced disease. We aimed to develop a deep learning (DL) program for classification of hepatic steatosis...

Diagnostic performance of an artificial intelligence model for the detection of pneumothorax at chest X-ray.

Clinical imaging
PURPOSE: Pneumothorax (PTX) is a common clinical urgency, its diagnosis is usually performed on chest radiography (CXR), and it presents a setting where artificial intelligence (AI) methods could find terrain in aiding radiologists in facing increasi...

Generalizable self-supervised learning for brain CTA in acute stroke.

Computers in biology and medicine
Acute stroke management involves rapid and accurate interpretation of CTA imaging data. However, generalizable models for multiple acute stroke tasks able to learn from unlabeled data do not exist. We propose a linear probed self-supervised contrasti...

Explaining deep learning models for age-related gait classification based on acceleration time series.

Computers in biology and medicine
BACKGROUND: Gait analysis holds significant importance in monitoring daily health, particularly among older adults. Advancements in sensor technology enable the capture of movement in real-life environments and generate big data. Machine learning, no...

Recognizing and explaining driving stress using a Shapley additive explanation model by fusing EEG and behavior signals.

Accident; analysis and prevention
Driving stress is a critical factor leading to road traffic accidents. Despite numerous studies that have been conducted on driving stress recognition, most of them only focus on accuracy improvement without taking model interpretability into account...

Segmentation of breast lesion using fuzzy thresholding and deep learning.

Computers in biology and medicine
Breast cancer is a major cause of morbidity and mortality in women. In breast cancer screening, Dynamic Contrast Enhanced Magnetic Resonance Imaging (DCE-MRI) has shown promise as a technique, providing enhanced temporal patterns of breast tissues. T...