AIMC Topic: Female

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Artificial Intelligence and Machine Learning in Preeclampsia.

Arteriosclerosis, thrombosis, and vascular biology
Preeclampsia is a multisystem hypertensive disorder that manifests itself after 20 weeks of pregnancy, along with proteinuria. The pathophysiology of preeclampsia is incompletely understood. Artificial intelligence, especially machine learning with i...

Use of Machine Learning to Assess the Management of Uncomplicated Urinary Tract Infection.

JAMA network open
IMPORTANCE: Uncomplicated urinary tract infection (UTI) is a common indication for outpatient antimicrobial therapy. National guidelines for the management of uncomplicated UTI were published in 2011, but the extent to which they align with current p...

Effective Dose Estimation in Computed Tomography by Machine Learning.

Tomography (Ann Arbor, Mich.)
BACKGROUND: Computed tomography scans are widely used in everyday medical practice due to speed, image reliability, and detectability of a wide range of pathologies. Each scan exposes the patient to a radiation dose, and performing a fast estimation ...

Stress Monitoring in Pandemic Screening: Insights from GSR Sensor and Machine Learning Analysis.

Biosensors
This study investigates the impact of patient stress on COVID-19 screening. An attempt was made to measure the level of anxiety of individuals undertaking rapid tests for SARS-CoV-2. To this end, a galvanic skin response (GSR) sensor that was connect...

Estimating Visual Acuity With Spectacle Correction From Fundus Photos Using Artificial Intelligence.

JAMA network open
IMPORTANCE: Determining spectacle-corrected visual acuity (VA) is essential when managing many ophthalmic diseases. If artificial intelligence (AI) evaluations of macular images estimated this VA from a fundus image, AI might provide spectacle-correc...

Non-invasive blood glucose monitoring using PPG signals with various deep learning models and implementation using TinyML.

Scientific reports
Accurate and continuous blood glucose monitoring is essential for effective diabetes management, yet traditional finger pricking methods are often inconvenient and painful. To address this issue, photoplethysmography (PPG) presents a promising non-in...

Noninvasive diagnosis of significant liver fibrosis in patients with chronic hepatitis B using nomogram and machine learning models.

Scientific reports
This study aims to construct and validate noninvasive diagnosis models for evaluating significant liver fibrosis in patients with chronic hepatitis B (CHB). A cohort of 259 CHB patients were selected as research subjects. Through random grouping, 182...

Predicting autoimmune thyroiditis in primary Sjogren's syndrome patients using a random forest classifier: a retrospective study.

Arthritis research & therapy
BACKGROUND: Primary Sjogren's syndrome (pSS) and autoimmune thyroiditis (AIT) share overlapping genetic and immunological profiles. This retrospective study evaluates the efficacy of machine learning algorithms, with a focus on the Random Forest Clas...

Logical reasoning for human activity recognition based on multisource data from wearable device.

Scientific reports
Smart wearable devices detection and recording of people's everyday activities is critical for health monitoring, helping persons with disabilities, and providing care for the elderly. Most of the research that is being conducted uses a machine learn...