AIMC Journal:
International journal of medical informatics

Showing 151 to 160 of 372 articles

Deep learning-based platform performs high detection sensitivity of intracranial aneurysms in 3D brain TOF-MRA: An external clinical validation study.

International journal of medical informatics
PURPOSE: To evaluate the diagnostic efficacy of a developed artificial intelligence (AI) platform incorporating deep learning algorithms for the automated detection of intracranial aneurysms in time-of-flight (TOF) magnetic resonance angiography (MRA...

Risk prediction model of metabolic syndrome in perimenopausal women based on machine learning.

International journal of medical informatics
INTRODUCTION: Metabolic syndrome (MetS) is considered to be an important parameter of cardio-metabolic health and contributing to the development of atherosclerosis, type 2 diabetes. The incidence of MetS significantly increases in postmenopausal wom...

Development, evaluation and validation of machine learning models to predict hospitalizations of patients with coronary artery disease within the next 12 months.

International journal of medical informatics
BACKGROUND: Improved survival of patients after acute coronary syndromes, population growth, and overall life expectancy rise have led to a significant increase in the proportion of patients with stable coronary artery disease (CAD), creating a signi...

Generative artificial intelligence in healthcare: A scoping review on benefits, challenges and applications.

International journal of medical informatics
BACKGROUND: Generative artificial intelligence (GAI) is revolutionizing healthcare with solutions for complex challenges, enhancing diagnosis, treatment, and care through new data and insights. However, its integration raises questions about applicat...

Pain prediction model based on machine learning and SHAP values for elders with dementia in Taiwan.

International journal of medical informatics
INTRODUCTION: Pain conditions are common in elderly individuals, including those with dementia. However, symptoms associated with dementia may lead to poor recognition, assessment and management of pain. In this study, we incorporated the variables b...

Sensing technologies and machine learning methods for emotion recognition in autism: Systematic review.

International journal of medical informatics
BACKGROUND: Human Emotion Recognition (HER) has been a popular field of study in the past years. Despite the great progresses made so far, relatively little attention has been paid to the use of HER in autism. People with autism are known to face pro...

Combining deep neural networks, a rule-based expert system and targeted manual coding for ICD-10 coding causes of death of French death certificates from 2018 to 2019.

International journal of medical informatics
OBJECTIVE: For ICD-10 coding causes of death in France in 2018 and 2019, predictions by deep neural networks (DNNs) are employed in addition to fully automatic batch coding by a rule-based expert system and to interactive coding by the coding team fo...

Predicting the presence of adherent perinephric fat using MRI radiomics combined with machine learning.

International journal of medical informatics
OBJECTIVES: Adherent perinephric fat (APF) poses significant challenges to surgical procedures. This study aimed to evaluate the usefulness of machine learning algorithms combined with MRI-based radiomics features for predicting the presence of APF.

Artificial Intelligence-Driven Radiomics in Head and Neck Cancer: Current Status and Future Prospects.

International journal of medical informatics
BACKGROUND: Radiomics is a rapidly growing field used to leverage medical radiological images by extracting quantitative features. These are supposed to characterize a patient's phenotype, and when combined with artificial intelligence techniques, to...

Evaluation of large language models performance against humans for summarizing MRI knee radiology reports: A feasibility study.

International journal of medical informatics
OBJECTIVES: This study addresses the critical need for accurate summarization in radiology by comparing various Large Language Model (LLM)-based approaches for automatic summary generation. With the increasing volume of patient information, accuratel...