AIMC Topic: Male

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Targeted metabolomics combined with machine learning to identify and validate new biomarkers for early SLE diagnosis and disease activity.

Clinical immunology (Orlando, Fla.)
BACKGROUND: The early diagnosis of systemic lupus erythematosus (SLE) and the assessment of disease activity progression remain a great challenge. Targeted metabolomics has great potential to identify new biomarkers of SLE.

The application of deep learning in abdominal trauma diagnosis by CT imaging.

World journal of emergency surgery : WJES
BACKGROUND: Abdominal computed tomography (CT) scan is a crucial imaging modality for creating cross-sectional images of the abdominal area, particularly in cases of abdominal trauma, which is commonly encountered in traumatic injuries. However, inte...

The role of various physiological and bioelectrical parameters for estimating the weight status in infants and juveniles cohort from the Southern Cuba region: a machine learning study.

BMC pediatrics
OBJECTIVE: The search for other indicators to assess the weight status of individuals is important as it may provide more accurate information and assist in personalized medicine.This work is aimed to develop a machine learning predictions of weigh s...

Machine learning-empowered sleep staging classification using multi-modality signals.

BMC medical informatics and decision making
The goal is to enhance an automated sleep staging system's performance by leveraging the diverse signals captured through multi-modal polysomnography recordings. Three modalities of PSG signals, namely electroencephalogram (EEG), electrooculogram (EO...

Detecting emotions through EEG signals based on modified convolutional fuzzy neural network.

Scientific reports
Emotion is a human sense that can influence an individual's life quality in both positive and negative ways. The ability to distinguish different types of emotion can lead researchers to estimate the current situation of patients or the probability o...

Automated machine learning model for fundus image classification by health-care professionals with no coding experience.

Scientific reports
To assess the feasibility of code-free deep learning (CFDL) platforms in the prediction of binary outcomes from fundus images in ophthalmology, evaluating two distinct online-based platforms (Google Vertex and Amazon Rekognition), and two distinct da...

Skin Conductance-Based Acupoint and Non-Acupoint Recognition Using Machine Learning.

IEEE journal of biomedical and health informatics
Acupoints (APs) prove to have positive effects on disease diagnosis and treatment, while intelligent techniques for the automatic detection of APs are not yet mature, making them more dependent on manual positioning. In this paper, we realize the ski...

CiGNN: A Causality-Informed and Graph Neural Network Based Framework for Cuffless Continuous Blood Pressure Estimation.

IEEE journal of biomedical and health informatics
Causalityholds profound potentials to dissipate confusion and improve accuracy in cuffless continuous blood pressure (BP) estimation, an area often neglected in current research. In this study, we propose a two-stage framework, CiGNN, that seamlessly...

Cardiac Valve Event Timing in Echocardiography Using Deep Learning and Triplane Recordings.

IEEE journal of biomedical and health informatics
Cardiac valve event timing plays a crucial role when conducting clinical measurements using echocardiography. However, established automated approaches are limited by the need of external electrocardiogram sensors, and manual measurements often rely ...