AIMC Topic: Age Factors

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Age and gender-related changes in choroidal thickness: Insights from deep learning analysis of swept-source OCT images.

Photodiagnosis and photodynamic therapy
BACKGROUND: The choroid is a vital vascular layer of the eye, essential for maintaining ocular health. Understanding its structural variations, particularly choroidal thickness (CT), is crucial for the early detection of diseases, such as age-related...

Age group classification based on optical measurement of brain pulsation using machine learning.

Scientific reports
Optical techniques, such as functional near-infrared spectroscopy (fNIRS), contain high potential for the development of non-invasive wearable systems for evaluating cerebral vascular condition in aging, due to their portability and ability to monito...

TractGraphFormer: Anatomically informed hybrid graph CNN-transformer network for interpretable sex and age prediction from diffusion MRI tractography.

Medical image analysis
The relationship between brain connections and non-imaging phenotypes is increasingly studied using deep neural networks. However, the local and global properties of the brain's white matter networks are often overlooked in convolutional network desi...

Machine learning validation of a simple prediction model for the correlation between advanced age and clinical outcomes in patients with aneurysmal subarachnoid hemorrhage.

Neurosurgical review
Adverse effects of advanced age and poor initial neurological status on outcomes of patients with aneurysmal subarachnoid hemorrhage (SAH) have been documented. While a predictive model of the non-linear correlation between advanced age and clinical ...

Personalized treatment strategies for breast adenoid cystic carcinoma: A machine learning approach.

Breast (Edinburgh, Scotland)
BACKGROUND: Breast adenoid cystic carcinoma (BACC) is a rare subtype of breast cancer that accounts for less than 0.1 % of all cases. This study was designed to assess the efficacy of various treatment approaches for BACC and to create the first web-...

Exploring the influence of age on the causes of death in advanced nasopharyngeal carcinoma patients undergoing chemoradiotherapy using machine learning methods.

Scientific reports
The present study analyzed the impact of age on the causes of death (CODs) in patients with nasopharyngeal carcinoma (NPC) undergoing chemoradiotherapy (CRT) using machine learning approaches. A total of 2841 patients (1037 classified as older, ≥ 60 ...

Predicting Age and Visual-Motor Integration Using Origami Photographs: Deep Learning Study.

JMIR formative research
BACKGROUND: Origami is a popular activity among preschool children and can be used by therapists as an evaluation tool to assess children's development in clinical settings. It is easy to implement, appealing to children, and time-efficient, requirin...

Machine learning-derived asthma and allergy trajectories in children: a systematic review and meta-analysis.

European respiratory review : an official journal of the European Respiratory Society
INTRODUCTION: Numerous studies have characterised trajectories of asthma and allergy in children using machine learning, but with different techniques and mixed findings. The present work aimed to summarise the evidence and critically appraise the me...

The association between Vitamin D deficiency and clinical pregnancy rate in IVF patients with different age.

Frontiers in endocrinology
BACKGROUND: The aim of the present study was to investigate the impact of serum VD status on IVF outcomes and to observe the effect of VD deficiency on the expression of the endometrial receptivity marker HOXA10.