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A machine learning artefact detection method for single-channel infant event-related potential studies.

Journal of neural engineering
. Automated detection of artefact in stimulus-evoked electroencephalographic (EEG) data recorded in neonates will improve the reproducibility and speed of analysis in clinical research compared with manual identification of artefact. Some studies use...

Deriving Automated Device Metadata From Intracranial Pressure Waveforms: A Transforming Research and Clinical Knowledge in Traumatic Brain Injury ICU Physiology Cohort Analysis.

Critical care explorations
IMPORTANCE: Treatment for intracranial pressure (ICP) has been increasingly informed by machine learning (ML)-derived ICP waveform characteristics. There are gaps, however, in understanding how ICP monitor type may bias waveform characteristics used ...

Explainable machine learning on baseline MRI predicts multiple sclerosis trajectory descriptors.

PloS one
Multiple sclerosis (MS) is a multifaceted neurological condition characterized by challenges in timely diagnosis and personalized patient management. The application of Artificial Intelligence (AI) to MS holds promises for early detection, accurate d...

Deep Learning Assisted Classification of T1ρ-MR Based Intervertebral Disc Degeneration Phases.

Journal of magnetic resonance imaging : JMRI
BACKGROUND: According to the T1ρ value of nucleus pulposus, our previous study has found that intervertebral disc degeneration (IDD) can be divided into three phases based on T1ρ-MR, which is helpful for the selection of biomaterial treatment timing....

Deep learning and explainable artificial intelligence for investigating dental professionals' satisfaction with CAD software performance.

Journal of prosthodontics : official journal of the American College of Prosthodontists
PURPOSE: This study aimed to examine the satisfaction of dental professionals, including dental students, dentists, and dental technicians, with computer-aided design (CAD) software performance using deep learning (DL) and explainable artificial inte...

Enhanced enchondroma detection from x-ray images using deep learning: A step towards accurate and cost-effective diagnosis.

Journal of orthopaedic research : official publication of the Orthopaedic Research Society
This study investigates the automated detection of enchondromas, benign cartilage tumors, from x-ray images using deep learning techniques. Enchondromas pose diagnostic challenges due to their potential for malignant transformation and overlapping ra...

Examining individual and contextual predictors of disability in Chinese older adults: A machine learning approach.

International journal of medical informatics
BACKGROUND: There is a large gap of understanding the determinants of disability, especially the contextual characteristics. Therefore, this study aimed to examine the important predictors of disability in Chinese older adults based on the social eco...

Diagnostic utility of transfer learning by using convolutional neural network for cytological diagnosis of malignant effusions.

Diagnostic cytopathology
INTRODUCTION: Cytological analysis of effusion specimens provides critical information regarding the diagnosis and staging of malignancies, thus guiding their treatment and subsequent monitoring. Keeping in view the challenges encountered in the morp...

A transparent machine learning algorithm uncovers HbA1c patterns associated with therapeutic inertia in patients with type 2 diabetes and failure of metformin monotherapy.

International journal of medical informatics
AIMS: This study aimed to identify and categorize the determinants influencing the intensification of therapy in Type 2 Diabetes (T2D) patients with suboptimal blood glucose control despite metformin monotherapy.