AIMC Topic: Adult

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An Examination of Temporomandibular Joint Disc Displacement through Magnetic Resonance Imaging by Integrating Artificial Intelligence: Preliminary Findings.

Medicina (Kaunas, Lithuania)
This research was aimed at constructing a complete automated temporomandibular joint disc position identification system that could assist with magnetic resonance imaging disc displacement diagnosis on oblique sagittal and oblique coronal images. T...

Ultrasonic Assessment of Liver Fibrosis Using One-Dimensional Convolutional Neural Networks Based on Frequency Spectra of Radiofrequency Signals with Deep Learning Segmentation of Liver Regions in B-Mode Images: A Feasibility Study.

Sensors (Basel, Switzerland)
The early detection of liver fibrosis is of significant importance. Deep learning analysis of ultrasound backscattered radiofrequency (RF) signals is emerging for tissue characterization as the RF signals carry abundant information related to tissue ...

Long COVID diagnostic with differentiation from chronic lyme disease using machine learning and cytokine hubs.

Scientific reports
The absence of a long COVID (LC) or post-acute sequelae of COVID-19 (PASC) diagnostic has profound implications for research and potential therapeutics given the lack of specificity with symptom-based identification of LC and the overlap of symptoms ...

Enhanced machine learning approaches for OSA patient screening: model development and validation study.

Scientific reports
Age, gender, body mass index (BMI), and mean heart rate during sleep were found to be risk factors for obstructive sleep apnea (OSA), and a variety of methods have been applied to predict the occurrence of OSA. This study aimed to develop and evaluat...

Deep learning based approach: automated gingival inflammation grading model using gingival removal strategy.

Scientific reports
Gingival inflammation grade serves as a well-established index in periodontitis. The aim of this study was to develop a deep learning network utilizing a novel feature extraction method for the automatic assessment of gingival inflammation. T-distrib...

Effects of machine learning errors on human decision-making: manipulations of model accuracy, error types, and error importance.

Cognitive research: principles and implications
This study addressed the cognitive impacts of providing correct and incorrect machine learning (ML) outputs in support of an object detection task. The study consisted of five experiments that manipulated the accuracy and importance of mock ML output...

What could be the role of genetic tests and machine learning of AXIN2 variant dominance in non-syndromic hypodontia? A case-control study in orthodontically treated patients.

Progress in orthodontics
BACKGROUND: Hypodontia is the most prevalent dental anomaly in humans, and is primarily attributed to genetic factors. Although genome-wide association studies (GWAS) have identified single-nucleotide polymorphisms (SNP) associated with hypodontia, g...

Online Privacy-Preserving EEG Classification by Source-Free Transfer Learning.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
Electroencephalogram (EEG) signals play an important role in brain-computer interface (BCI) applications. Recent studies have utilized transfer learning to assist the learning task in the new subject, i.e., target domain, by leveraging beneficial inf...

Digital Biomarker for Muscle Function Assessment Using Surface Electromyography With Electrical Stimulation and a Non-Invasive Wearable Device.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
Sarcopenia is a comprehensive degenerative disease with the progressive loss of skeletal muscle mass with age, accompanied by the loss of muscle strength and muscle dysfunction. Individuals with unmanaged sarcopenia may experience adverse outcomes. P...

Optimizing number of Raman spectra using an artificial neural network guided Monte Carlo simulation approach to analyze human cortical bone.

Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy
This study presents a novel methodology for optimizing the number of Raman spectra required per sample for human bone compositional analysis. The methodology integrates Artificial Neural Network (ANN) and Monte Carlo Simulation (MCS). We demonstrate ...