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Generative AI mitigates representation bias and improves model fairness through synthetic health data.

PLoS computational biology
Representation bias in health data can lead to unfair decisions and compromise the generalisability of research findings. As a consequence, underrepresented subpopulations, such as those from specific ethnic backgrounds or genders, do not benefit equ...

Investigating the Link between Type 2 Diabetes and Epstein-Barr Virus: a Machine Learning and Mendelian Randomization.

Clinical laboratory
BACKGROUND: Epstein-Barr virus (EBV) is a ubiquitous herpesvirus that is known to cause infectious mononucleosis and is associated with several autoimmune diseases and cancers through immune system dysregulation and chronic inflammatory mechanisms.

[Evaluation of the possibility of using neural networks for automatic diagnostics of obstructive urination].

Urologiia (Moscow, Russia : 1999)
INTRODUCTION: Obstructive type of urination requires accurate and timely diagnosis to prevent complications and improve the quality of life of patients. Traditional diagnostic methods such as uroflowmetry, although they remain the standard, have thei...

[Usage of artificial intelligence in the clinical practice of urologists in observations with renal parenchymal neoplasms].

Urologiia (Moscow, Russia : 1999)
OBJECTIVE: to assess the needs and attitudes of urologists regarding the use of technologies related to artificial intelligence, particularly the web platform "Sechenov.AI_nephro", in the surgical treatment of patients with renal parenchymal neoplasm...

Deep Learning Study of Alkaptonuria Spinal Disease Assesses Global and Regional Severity and Detects Occult Treatment Status.

Journal of inherited metabolic disease
Deep learning (DL) is increasingly used to analyze medical imaging, but is less refined for rare conditions, which require novel pre-processing and analytical approaches. To assess DL in the context of rare diseases, this study focused on alkaptonuri...

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...

The effects of the post-delay epochs on working memory error reduction.

PLoS computational biology
Accurate retrieval of the maintained information is crucial for working memory. This process primarily occurs during post-delay epochs, when subjects receive cues and generate responses. However, the computational and neural mechanisms that underlie ...

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...