AIMC Topic: Young Adult

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CATALYZE: a deep learning approach for cataract assessment and grading on SS-OCT images.

Journal of cataract and refractive surgery
PURPOSE: To assess a new objective deep learning model cataract grading method based on swept-source optical coherence tomography (SS-OCT) scans provided by the Anterion.

Stimulus Selection Influences Prediction of Individual Phenotypes in Naturalistic Conditions.

Human brain mapping
While the use of naturalistic stimuli such as movie clips for understanding individual differences and brain-behaviour relationships attracts increasing interest, the influence of stimulus selection remains largely unclear. By using machine learning ...

Detection and classification of glomerular lesions in kidney graft biopsies using 2-stage deep learning approach.

Medicine
Acute allograft rejection in patients undergoing renal transplantation is diagnosed through histopathological analysis of renal graft biopsies, which can be used to quantify elementary lesions. However, quantification of elementary lesions requires c...

Tracking vigilance fluctuations in real-time: a sliding-window heart rate variability-based machine-learning approach.

Sleep
STUDY OBJECTIVES: Heart rate variability (HRV)-based machine learning models hold promise for real-world vigilance evaluation, yet their real-time applicability is limited by lengthy feature extraction times and reliance on subjective benchmarks. Thi...

MDD-SSTNet: detecting major depressive disorder by exploring spectral-spatial-temporal information on resting-state electroencephalography data based on deep neural network.

Cerebral cortex (New York, N.Y. : 1991)
Major depressive disorder (MDD) is a psychiatric disorder characterized by persistent lethargy that can lead to suicide in severe cases. Hence, timely and accurate diagnosis and treatment are crucial. Previous neuroscience studies have demonstrated t...

Prevalence, incidence, and mortality of inflammatory bowel disease in the Netherlands: development and external validation of machine learning models.

Journal of Crohn's & colitis
BACKGROUND AND AIMS: Large registries are promising tools to study the epidemiology of inflammatory bowel disease (IBD). We aimed to develop and validate machine learning models to identify IBD cases in administrative data, aiming to determine the pr...

New Method of Early RRMS Diagnosis Using OCT-Assessed Structural Retinal Data and Explainable Artificial Intelligence.

Translational vision science & technology
PURPOSE: The purpose of this study was to provide the development of a method to classify optical coherence tomography (OCT)-assessed retinal data in the context of automatic diagnosis of early-stage multiple sclerosis (MS) with decision explanation.

Advanced Artificial-Intelligence-Based Jiang Formula for Intraocular Lens Power in Congenital Ectopia Lentis.

Translational vision science & technology
PURPOSE: The purpose of this study was to develop an artificial intelligence (AI)-based intraocular lens (IOLs) power calculation formula for improving the accuracy of IOLs power calculations in patients with congenital ectopia lentis (CEL).

Speech Detection via Respiratory Inductance Plethysmography, Thoracic Impedance, Accelerometers, and Gyroscopes: A Machine Learning-Informed Comparative Study.

Psychophysiology
Speech production interferes with the measurement of changes in cardiac vagal activity during acute stress by attenuating the expected drop in heart rate variability. Speech also induces cardiac sympathetic changes similar to those induced by psychol...

Identifying Preliminary Risk Profiles for Dissociation in 16- to 25-Year-Olds Using Machine Learning.

Early intervention in psychiatry
INTRODUCTION: Dissociation is associated with clinical severity, increased risk of suicide and self-harm, and disproportionately affects adolescents and young adults. Whilst evidence indicates multiple factors contribute to dissociative experiences, ...