AIMC Topic: Female

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Exploration of immune-related diagnostic biomarkers in unexplained infertility by bioinformatics analysis and machine learning.

Taiwanese journal of obstetrics & gynecology
OBJECTIVE: We aimed to discover the biomarkers associated with UI and their correlation with immune cell infiltration.

Identification of a Biomarker Panel in Extracellular Vesicles Derived From Non-Small Cell Lung Cancer (NSCLC) Through Proteomic Analysis and Machine Learning.

Journal of extracellular vesicles
Antigen fingerprint profiling of tumour-derived extracellular vesicles (TDEVs) in the body fluids is a promising strategy for identifying tumour biomarkers. In this study, proteomic and immunological assays reveal significantly higher CD155 levels in...

Diagnostic Accuracy of On-Premise Automated Coronary CT Angiography Analysis Based on Coronary Artery Disease Reporting and Data System 2.0.

Radiology
Background Chest pain is a leading cause of outpatient and emergency department visits; advancements in artificial intelligence (AI) could improve coronary CT angiography (CCTA) workflows for these patients. Purpose To evaluate the performance of an ...

Association of Deep Learning-based Chest CT-derived Respiratory Parameters with Disease Progression in Amyotrophic Lateral Sclerosis.

Radiology
Background Forced vital capacity (FVC) is a standard measure of respiratory function in patients with amyotrophic lateral sclerosis (ALS) but has limitations, particularly for patients with bulbar impairment. Purpose To determine the value of deep le...

Machine-learning based classification of middle-ear fixation and separation using sweep frequency impedance information reflecting middle-ear dynamics.

The Journal of the Acoustical Society of America
The sweep frequency impedance (SFI) meter is an apparatus that delivers a frequency-sweeping sound into the ear canal and evaluates dynamic characteristics of the middle ear based on changes in sound pressure in the ear canal. We have renewed the SFI...

Machine Learning-Based Identification of Children With Intermittent Exotropia Using Multiple Resting-State Functional Magnetic Resonance Imaging Features.

Brain and behavior
OBJECTIVE: To investigate the performance of machine learning (ML) methods based on resting-state functional magnetic resonance imaging (rs-fMRI) parameters in distinguishing children with intermittent exotropia (IXT) from healthy controls (HCs).

Advancing Nutritional Status Classification With Hybrid Artificial Intelligence: A Novel Methodological Approach.

Brain and behavior
PURPOSE: Malnutrition remains a critical public health issue in low-income countries, significantly hindering economic development and contributing to over 50% of infant deaths. Under nutrition weakens immune systems, increasing susceptibility to com...

Early detection of Alzheimer's disease using deep learning methods.

Alzheimer's & dementia : the journal of the Alzheimer's Association
INTRODUCTION: Alzheimer's disease (AD), a leading cause of dementia, requires early detection for effective intervention. This study employs AI to analyze multimodal datasets, including clinical, biomarker, and neuroimaging data, using hybrid deep le...

Impact of Blastocystis carriage and colonization intensity on gut microbiota composition in a non-westernized rural population from Colombia.

PLoS neglected tropical diseases
BACKGROUND: The role of Blastocystis, a common intestinal parasitic protist of humans and other animals, in human health and disease remains elusive. Recent studies suggest a connection between Blastocystis colonization, healthier lifestyles, and hig...

The role of the sacroiliac joint in sex estimation: Analysis of morphometry and variation types using machine learning techniques.

Legal medicine (Tokyo, Japan)
This study aimed to evaluate the potential of machine learning algorithms in sex estimation by going beyond the traditional two-dimensional (2D) measurements of the pelvic bone, predominantly preferred in sex prediction, by including measurement data...