AIMC Topic: Male

Clear Filters Showing 7841 to 7850 of 27433 articles

A machine learning tool for identifying newly diagnosed heart failure in individuals with known diabetes in primary care.

ESC heart failure
AIMS: We aimed to create a predictive model utilizing machine learning (ML) to identify new cases of congestive heart failure (CHF) in individuals with diabetes in primary health care (PHC) through the analysis of diagnostic data.

IPCT-Net: Parallel information bottleneck modality fusion network for obstructive sleep apnea diagnosis.

Neural networks : the official journal of the International Neural Network Society
Obstructive sleep apnea (OSA) is a common sleep breathing disorder and timely diagnosis helps to avoid the serious medical expenses caused by related complications. Existing deep learning (DL)-based methods primarily focus on single-modal models, whi...

Enhancing self-directed learning with custom GPT AI facilitation among medical students: A randomized controlled trial.

Medical teacher
OBJECTIVE: This study aims to assess the impact of LearnGuide, a specialized ChatGPT tool designed to support self-directed learning among medical students.

A deep learning-based, real-time image report system for linear EUS.

Gastrointestinal endoscopy
BACKGROUND AND AIMS: The integrity of image acquisition is critical for biliopancreatic EUS reporting, significantly affecting the quality of EUS examinations and disease-related decision-making. However, the quality of EUS reports varies among endos...

Development of machine learning model for predicting prolonged operation time in lumbar stenosis undergoing posterior lumbar interbody fusion: a multicenter study.

The spine journal : official journal of the North American Spine Society
BACKGROUND CONTEXT: Longer posterior lumbar interbody fusion (PLIF) surgeries for individuals with lumbar spinal stenosis are linked to more complications and negatively affect recovery after the operation. Therefore, there is a critical need for a m...

Grading of diabetic retinopathy using a pre-segmenting deep learning classification model: Validation of an automated algorithm.

Acta ophthalmologica
PURPOSE: To validate the performance of autonomous diabetic retinopathy (DR) grading by comparing a human grader and a self-developed deep-learning (DL) algorithm with gold-standard evaluation.

The Impact of Deep Learning on Determining the Necessity of Bronchoscopy in Pediatric Foreign Body Aspiration: Can Negative Bronchoscopy Rates Be Reduced?

Journal of pediatric surgery
INTRODUCTION: This study aimed to evaluate the role of deep learning methods in diagnosing foreign body aspiration (FBA) to reduce the frequency of negative bronchoscopy and minimize potential complications.

External evaluation of a commercial artificial intelligence-augmented digital auscultation platform in valvular heart disease detection using echocardiography as reference standard.

International journal of cardiology
OBJECTIVE: There are few studies evaluating the accuracy of commercially available AI-powered digital auscultation platforms in detecting valvular heart disease (VHD). Therefore, the utility of these systems for diagnosing clinically significant VHD ...

Multimodal radiomics and deep learning models for predicting early femoral head deformity in LCPD.

European journal of radiology
PURPOSE: To develop a predictive model combining clinical, radiomic, and deep learning features based on X-ray and MRI to identify risk factors for early femoral head deformity in Legg-Calvé-Perthes disease (LCPD).

Medical imaging and radiation science students' use of artificial intelligence for learning and assessment.

Radiography (London, England : 1995)
INTRODUCTION: Artificial intelligence has permeated all aspects of our existence, and medical imaging has shown the burgeoning use of artificial intelligence in clinical environments. However, there are limited empirical studies on radiography studen...