AIMC Topic: Female

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Demographic influences on trust in artificial intelligence across cognitive domains: A statistical perspective.

PloS one
As artificial intelligence (AI) systems become increasingly integrated into decision-making across various sectors, understanding public trust in these systems is more crucial than ever. This study presents a quantitative analysis of survey data from...

Development of a diagnostic model for ovarian cancer based on machine learning algorithms and functional analysis of key biomarker SOX17.

Journal of ovarian research
BACKGROUND: Ovarian cancer (OC) demonstrates the poorest prognosis among gynecological malignancies, with five-year survival rates below 45%, primarily due to late-stage diagnosis. To address this challenge, we systematically identified OC-specific d...

Differentiation of light chain cardiac amyloidosis and hypertrophic cardiomyopathy by ensemble machine learning-based radiomic analysis of cardiac magnetic resonance.

Orphanet journal of rare diseases
BACKGROUND: We aim to assess the diagnosis performance of an ensemble machine learning (ML) based radiomic analysis of multiparametric cardiac magnetic resonance (CMR) to differentiate light chain cardiac amyloidosis (AL-CA) and hypertrophic cardiomy...

Optimizing myocardial infarction detection: a hybrid CNN-GRU deep learning approach.

BMC medical informatics and decision making
BACKGROUND: Myocardial infarction (MI) is a life-threatening condition caused by sudden interruption of blood supply to the heart. Electrocardiogram (ECG) is the primary tool for MI diagnosis, but interpretation challenges exist. This study aimed to ...

CT radiomics-based explainable machine learning model for accurate differentiation of malignant and benign endometrial tumors: a two-center study.

Biomedical engineering online
OBJECTIVES: This study aimed to develop and validate a CT radiomics-based explainable machine learning model for precise diagnosing of malignancy and benignity specifically in endometrial cancer (EC) patients.

Development and validation of a multidimensional and interpretable artificial intelligence model to predict gout recurrence in hospitalised patients: a real-world, ambispective multicentre cohort study in China.

BMC medicine
BACKGROUND: Gout is the most common inflammatory arthritis. Recurrent flares are common among hospitalised patients and contribute to substantial clinical and economic burden. However, the accurate prediction of inpatient recurrence remains challengi...

Morphological alterations of peridroplet mitochondria in human liver biopsy.

Scientific reports
Mitochondrial dysfunction and the accumulation of lipid droplets (LD) contribute to the pathogenesis of liver diseases. Mitochondria bound to LD, termed peridroplet mitochondria (PDM), form a subpopulation with distinct functions compared to cytoplas...

Novel morphological indexes for quantitative evaluation of cerebral aneurysm irregularity.

Scientific reports
A cerebral aneurysm may present irregularities associated with rupture risks. However, conventional morphological parameters are limited in evaluating the aneurysm irregularity. Although the mass moment of inertia has been devised for the irregularit...

Identified endoplasmic reticulum stress-related molecular cluster and immune characterization in endometriosis.

Scientific reports
Endometriosis is a common disease among women of childbearing age, and endoplasmic reticulum stress (ERS), a response involved in regulating protein homeostasis, has been linked to its pathogenesis. To identify ERS-related hub genes, this study seque...

Development and validation of an interpretable machine learning model for early prediction in patients with diabetes and sepsis.

Scientific reports
We aimed to identify and validate key predictive factors influencing 28-day survival rates in patients with diabetes and sepsis and to develop a predictive model based on these factors to assist clinical decision-making. In this retrospective cohort ...