AIMC Topic: Middle Aged

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Deep learning-based quantification of osteonecrosis using magnetic resonance images in Gaucher disease.

Bone
Gaucher disease is one of the most common lysosomal storage disorders. Osteonecrosis is a principal clinical manifestation of Gaucher disease and often leads to joint collapse and fractures. T1-weighted (T1w) modality in MRI is widely used to monitor...

Automatic Recognition of Auditory Brainstem Response Waveforms Using a Deep Learning-Based Framework.

Otolaryngology--head and neck surgery : official journal of American Academy of Otolaryngology-Head and Neck Surgery
OBJECTIVE: Recognition of auditory brainstem response (ABR) waveforms may be challenging, particularly for older individuals or those with hearing loss. This study aimed to investigate deep learning frameworks to improve the automatic recognition of ...

Alterations in dynamic regional homogeneity within default mode network in patients with thyroid-associated ophthalmopathy.

Neuroreport
Thyroid-associated ophthalmopathy (TAO) is a significant autoimmune eye disease known for causing exophthalmos and substantial optic nerve damage. Prior investigations have solely focused on static functional MRI (fMRI) scans of the brain in TAO pati...

Machine learning-based model for predicting outcomes in cerebral hemorrhage patients with leukemia.

European journal of radiology
BACKGROUND AND PURPOSE: Intracranial hemorrhage (ICH) in leukemia patients progresses rapidly with high mortality. Limited data are available on imaging studies in this population. The study aims to develop prediction models for 7-day and short-term ...

EEG based functional connectivity in resting and emotional states may identify major depressive disorder using machine learning.

Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology
OBJECTIVE: Disrupted brain network connectivity underlies major depressive disorder (MDD). Altered EEG based Functional connectivity (FC) with Emotional stimuli in major depressive disorder (MDD) in addition to resting state FC may help in improving ...

Construction and validation of a clinical risk model based on machine learning for screening characteristic factors of lymphovascular space invasion in endometrial cancer.

Scientific reports
This study aimed to identify factors that affect lymphovascular space invasion (LVSI) in endometrial cancer (EC) using machine learning technology, and to build a clinical risk assessment model based on these factors. Samples were collected from May ...

Relationships between minerals' intake and blood homocysteine levels based on three machine learning methods: a large cross-sectional study.

Nutrition & diabetes
BACKGROUND: Blood homocysteine (Hcy) level has become a sensitive indicator in predicting the development of cardiovascular disease. Studies have shown an association between individual mineral intake and blood Hcy levels. The effect of mixed mineral...

A miRNA-based epigenetic molecular clock for biological skin-age prediction.

Archives of dermatological research
Skin aging is one of the visible characteristics of the aging process in humans. In recent years, different biological clocks have been generated based on protein or epigenetic markers, but few have focused on biological age in the skin. Arrest the a...

Radiomics-based machine learning approach for the prediction of grade and stage in upper urinary tract urothelial carcinoma: a step towards virtual biopsy.

International journal of surgery (London, England)
OBJECTIVES: Upper tract urothelial carcinoma (UTUC) is a rare, aggressive lesion, with early detection a key to its management. This study aimed to utilise computed tomographic urogram data to develop machine learning models for predicting tumour gra...