AIMC Topic: Machine Learning

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Systematic Review of Retinal Blood Vessels Segmentation Based on AI-driven Technique.

Journal of imaging informatics in medicine
Image segmentation is a crucial task in computer vision and image processing, with numerous segmentation algorithms being found in the literature. It has important applications in scene understanding, medical image analysis, robotic perception, video...

Development of a machine learning-based model for predicting individual responses to antihypertensive treatments.

Nutrition, metabolism, and cardiovascular diseases : NMCD
BACKGROUND AND AIMS: Personalized antihypertensive drug selection is essential for optimizing hypertension management. The study aimed to develop a machine learning (ML) model to predict individual blood pressure (BP) responses to different antihyper...

Performance of different machine learning algorithms in identifying undiagnosed diabetes based on nonlaboratory parameters and the influence of muscle strength: A cross-sectional study.

Journal of diabetes investigation
AIMS/INTRODUCTION: Machine learning algorithms based on the artificial neural network (ANN), support vector machine, naive Bayesian or logistic regression model are commonly used to identify diabetes. This study investigated which approach performed ...

Metabolomics profile and machine learning prediction of treatment responses in immune thrombocytopenia: A prospective cohort study.

British journal of haematology
Immune thrombocytopenia (ITP) is an autoimmune disease characterized by antibody-mediated platelet destruction and impaired platelet production. The mechanisms underlying ITP and biomarkers predicting the response of drug treatments are elusive. We p...

Systemic lupus in the era of machine learning medicine.

Lupus science & medicine
Artificial intelligence and machine learning applications are emerging as transformative technologies in medicine. With greater access to a diverse range of big datasets, researchers are turning to these powerful techniques for data analysis. Machine...

Data extraction for evidence synthesis using a large language model: A proof-of-concept study.

Research synthesis methods
Data extraction is a crucial, yet labor-intensive and error-prone part of evidence synthesis. To date, efforts to harness machine learning for enhancing efficiency of the data extraction process have fallen short of achieving sufficient accuracy and ...

High-spatial resolution ground-level ozone in Yunnan, China: A spatiotemporal estimation based on comparative analyses of machine learning models.

Environmental research
Monitoring ground-level ozone concentrations is a critical aspect of atmospheric environmental studies. Given the existing limitations of satellite data products, especially the lack of ground-level ozone characterization, and the discontinuity of gr...

Machine-Learning-Accelerated Simulations for the Design of Airbag Constrained by Obstacles at Rest.

Stapp car crash journal
Predicting airbag deployment geometries is an important task for airbag and vehicle designers to meet safety standards based on biomechanical injury risk functions. This prediction is also an extraordinarily complex problem given the number of discip...

Measuring functional hand use in children with unilateral cerebral palsy using accelerometry and machine learning.

Developmental medicine and child neurology
AIM: To investigate wearable sensors for measuring functional hand use in children with unilateral cerebral palsy (CP).