AIMC Topic: Adult

Clear Filters Showing 8061 to 8070 of 15606 articles

Defining Normal Ranges of Skeletal Muscle Area and Skeletal Muscle Index in Children on CT Using an Automated Deep Learning Pipeline: Implications for Sarcopenia Diagnosis.

AJR. American journal of roentgenology
Skeletal muscle area (SMA), representing skeletal muscle cross-sectional area at the L3 vertebral level, and skeletal muscle index (SMI), representing height-normalized SMA, can serve as markers of sarcopenia. Normal SMA and SMI values have been rep...

Systematic Review and Comparison of Publicly Available ICU Data Sets-A Decision Guide for Clinicians and Data Scientists.

Critical care medicine
OBJECTIVE: As data science and artificial intelligence continue to rapidly gain traction, the publication of freely available ICU datasets has become invaluable to propel data-driven clinical research. In this guide for clinicians and researchers, we...

Prediction of pouchitis after ileal pouch-anal anastomosis in patients with ulcerative colitis using artificial intelligence and deep learning.

Techniques in coloproctology
BACKGROUND: Pouchitis is one of the major postoperative complications of ulcerative colitis (UC), and it is still difficult to predict the development of pouchitis after ileal pouch-anal anastomosis (IPAA) in UC patients. In this study, we examined w...

The Feasibility of a Machine Learning Approach in Predicting Successful Ventilator Mode Shifting for Adult Patients in the Medical Intensive Care Unit.

Medicina (Kaunas, Lithuania)
: Traditional assessment of the readiness for the weaning from the mechanical ventilator (MV) needs respiratory parameters in a spontaneous breath. Exempted from the MV disconnecting and manual measurements of weaning parameters, a prediction model b...

Automatic detection of passing and shooting in water polo using machine learning: a feasibility study.

Sports biomechanics
There is currently no efficient way to quantify overhead throwing volume in water polo. Therefore, this study aimed to test the feasibility of a method to detect passes and shots in water polo automatically using inertial measurement units (IMU) and ...

Deep-learning model for predicting the survival of rectal adenocarcinoma patients based on a surveillance, epidemiology, and end results analysis.

BMC cancer
BACKGROUND: We collected information on patients with rectal adenocarcinoma in the United States from the Surveillance, Epidemiology, and EndResults (SEER) database. We used this information to establish a model that combined deep learning with a mul...

Classification of Electroencephalogram Signal for Developing Brain-Computer Interface Using Bioinspired Machine Learning Approach.

Computational intelligence and neuroscience
Transforming human intentions into patterns to direct the devices connected externally without any body movements is called Brain-Computer Interface (BCI). It is specially designed for rehabilitation patients to overcome their disabilities. Electroen...

Assessing port service quality: An application of the extension fuzzy AHP and importance-performance analysis.

PloS one
It is argued that ports are playing a crucial role in developing nations' economy. Still, solutions to improving port service quality (PSQ) to boost ports' competitive capacity is questionable. Hence, this study aims to investigate port service quali...

Impact of Age and Sex on COVID-19 Severity Assessed From Radiologic and Clinical Findings.

Frontiers in cellular and infection microbiology
BACKGROUND: Data on the epidemiological characteristics and clinical features of COVID-19 in patients of different ages and sex are limited. Existing studies have mainly focused on the pediatric and elderly population.

Use of deep learning in the MRI diagnosis of Chiari malformation type I.

Neuroradiology
PURPOSE: To train deep learning convolutional neural network (CNN) models for classification of clinically significant Chiari malformation type I (CM1) on MRI to assist clinicians in diagnosis and decision making.