AIMC Topic: Humans

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A Comparative Analysis of Three Large Language Models on Bruxism Knowledge.

Journal of oral rehabilitation
BACKGROUND: Artificial Intelligence (AI) has been widely used in health research, but the effectiveness of large language models (LLMs) in providing accurate information on bruxism has not yet been evaluated.

Excited state kinetics of tryptophan and NAD(P)H in blood plasma of normal and abnormal liver conditions: A tool to understand the metabolic changes and classification.

Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy
Early diagnosis at the metabolomic level is crucial for the treatment of liver cirrhosis and hepatocellular carcinoma (HCC). In this study, attempts were made to investigate the excited-state kinetics of intrinsic fluorophores, tryptophan and nicotin...

Deep-learning-based diagnosis framework for ankle-brachial index defined peripheral arterial disease of lower extremity wound: Comparison with physicians.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Few studies have evaluated peripheral artery disease (PAD) in patients with lower extremity wounds by a convolutional neural network (CNN)-based deep learning algorithm. We aimed to establish a framework for PAD detection, p...

Do Machine Learning Approaches Perform Better Than Regression Models in Mapping Studies? A Systematic Review.

Value in health : the journal of the International Society for Pharmacoeconomics and Outcomes Research
OBJECTIVES: To identify how machine learning (ML) approaches were implemented in mapping studies and to determine the extent to which ML improved performance compared with regression models (RMs).

Predicting cognitive decline: Deep-learning reveals subtle brain changes in pre-MCI stage.

The journal of prevention of Alzheimer's disease
BACKGROUND: Mild cognitive impairment (MCI) and preclinical MCI (e.g., subjective cognitive decline, SCD) are considered risk states of dementia, such as Alzheimer's Disease (AD). However, it is challenging to accurately predict conversion from norma...

Machine learning-driven Heckmatt grading in facioscapulohumeral muscular dystrophy: A novel pathway for musculoskeletal ultrasound analysis.

Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology
OBJECTIVE: This study introduces a machine learning approach to automate muscle ultrasound analysis, aiming to improve objectivity and efficiency in segmentation, classification, and Heckmatt grading.

SegmentAnyBone: A universal model that segments any bone at any location on MRI.

Medical image analysis
Magnetic Resonance Imaging (MRI) is pivotal in radiology, offering non-invasive and high-quality insights into the human body. Precise segmentation of the MRIs into different organs and tissues would be very beneficial as it would allow more accurate...

TQGDNet: Coronary artery calcium deposit detection on computed tomography.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Coronary artery disease (CAD) continues to be a leading global cause of cardiovascular related mortality. The scoring of coronary artery calcium (CAC) using computer tomography (CT) images is a diagnostic instrument for evaluating the risk of asympto...

Towards robotic Laboratory Automation Plug & Play: LAPP reference implementation with the TIAGo mobile manipulator.

SLAS technology
Laboratory automation with mobile manipulators presents a promising avenue for enhancing efficiency and reducing the dependency on human activity in laboratory workflows. To achieve seamless integration, the Laboratory Automation Plag & Play concept ...

Video Clip Extraction From Fetal Ultrasound Scans Using Artificial Intelligence to Allow Remote Second Expert Review for Congenital Heart Disease.

Prenatal diagnosis
OBJECTIVE: To use artificial intelligence (AI) to automatically extract video clips of the fetal heart from a stream of ultrasound video, and to assess the performance of these when used for remote second review.