AIMC Topic: Female

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Prediction of the ectasia screening index from raw Casia2 volume data for keratoconus identification by using convolutional neural networks.

PloS one
Purpose Prediction of the ectasia screening index, an estimator provided by the Casia2 instrument for identifying keratoconus, from raw optical coherence tomography data using convolutional neural networks. Methods Three convolutional neural networks...

Deep learning methods to forecasting human embryo development in time-lapse videos.

PloS one
BACKGROUND: In assisted reproductive technology, evaluating the quality of the embryo is crucial when selecting the most viable embryo for transferring to a woman. Assessment also plays an important role in determining the optimal transfer time, eith...

Visual processing oscillates differently through time for adults with ADHD.

PloS one
ADHD is a neurodevelopmental disorder affecting 3-4% of Canadian adults and 2.6% of adults worldwide. Its symptoms include inattention, hyperactivity and impulsivity. Though ADHD is known to affect several brain functions and cognitive processes, lit...

Deep learning detection of retinal detachment: Optical coherence tomography staging and estimation of duration of macular detachment.

PloS one
OBJECTIVE: To test the applicability of deep learning models for detecting and staging rhegmatogenous retinal detachment (RRD) based on morphological features using two- and three-dimensional optical coherence tomography (OCT) scans.

Predicting 30-day hospital readmissions using ClinicalT5 with structured and unstructured electronic health records.

PloS one
Hospital readmission prediction is a crucial area of research due to its impact on healthcare expenditure, patient care quality, and policy formulation. Accurate prediction of patient readmissions within 30 days post-discharge remains a considerable ...

Development and validation of a deep learning-based assessment tool for teacher leadership: A case study from Xinjiang, China.

PloS one
Teacher leadership is widely regarded as a critical driver of school reform and educational quality improvement. Although the field has been extensively studied, empirical research remains limited in Xinjiang, China-a region characterized by its mult...

Improving attachment style clustering with ROCKET and CatBoost: Insights from EEG analysis.

PloS one
Understanding attachment styles is essential in psychology and neuroscience, yet predicting them using objective neural data remains challenging. This study explores the use of machine learning (ML) models and EEG analysis to improve attachment style...

Association between hemoglobin glycation index and 28-day all-cause mortality in acute myocardial infarction patients: Analysis of the MIMIC-IV database.

PloS one
Acute myocardial infarction (AMI) substantially fuels the worldwide escalation in both morbidity and mortality. The hemoglobin glycation index (HGI) is linked to a range of undesirable outcomes, but its relationship with short-term outcomes in AMI pa...

Phenotypic Selectivity of Artificial Intelligence-Enhanced Electrocardiography in Cardiovascular Diagnosis and Risk Prediction.

Circulation
BACKGROUND: Artificial intelligence (AI)-enhanced ECG (AI-ECG) models are often designed to detect specific anatomical and functional cardiac abnormalities. Understanding the selectivity of their phenotypic associations is essential to inform their c...

Effects of walking training with and without a robot and standard care on clinical and mobility outcomes: A randomized clinical trial in acute ischemic stroke patients.

Experimental gerontology
BACKGROUND: Stroke incidence rises with age. A stroke can severely affect walking ability, requiring therapy. Robot-assisted walking therapy (ROB) has been advocated as one form of walking rehabilitation in stroke patients. However, its comparative e...