AIMC Topic: Middle Aged

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Comparative analysis of machine learning-derived nomogram and biomarkers in predicting side-specific extraprostatic extension: Preliminary findings.

Clinical imaging
AIM: This study aimed to assess and compare the performance of nomograms and machine learning (ML) techniques using preoperative biomarkers for predicting side-specific extraprostatic extension (EPE) in prostate cancer, which is linked to poor outcom...

Evaluation of meibomian gland dysfunction with deep learning model considering different datasets and gland morphology.

Computers in biology and medicine
Meibomian gland dysfunction (MGD) is recognized as the primary cause of evaporative-type dry eye disease (DED). Diagnosis typically involves assessing meibomian gland (MG) morphology alongside symptom evaluation. Traditionally, experts manually grade...

Classification of knee osteoarthritis severity using markerless motion capture and long short-term memory fully convolutional network.

Computers in biology and medicine
This study explored the integration of markerless motion capture and deep learning to classify knee osteoarthritis severity based on gait kinematics, providing an alternative to traditional assessment methods. We employed a Long Short-Term Memory Ful...

Early detection of Alzheimer's disease using small RNAs. Results from the EPAD cohort.

The journal of prevention of Alzheimer's disease
BACKGROUND: Alzheimer's disease (AD) is the most common form of dementia, and early diagnosis is crucial to enable effective interventions. Currently, Alzheimer's disease is diagnosed through cognitive assessments, brain imaging and fluid biomarkers ...

The diagnostic model from semi-supervised cross modality transformation improved the distinguished ability of X-rays for pulmonary tuberculosis.

Clinical radiology
BACKGROUND: Early diagnosis of tuberculosis is particularly difficult in resource-poor areas. Traditional chest X-rays (CXR) have limited accuracy, while CT scans are costly and involve radiation exposure. The study aims to improve the diagnostic acc...

Explainable classification of Parkinson's disease with different motor subtypes by analyzing the synthetic MRI quantitative parameters of subcortical nuclei.

European journal of radiology
OBJECTIVES: To explore differences in quantitative parameters of subcortical nuclei using synthetic MRI across different motor subtypes of Parkinson's Disease (PD), and to develop an interpretable model for distinguishing PD subtypes.

Task-specific versus general-purpose AI models in ECG analysis: A comparative study with emergency medicine specialists.

The American journal of emergency medicine
PURPOSE: To evaluate and compare the diagnostic accuracy of three Artificial intelligence (AI) models-GPT-4o, Canva-GPT, and ECG Reader-GPT-against emergency medicine specialists (EMSs) in electrocardiogram (ECG) interpretation using a standardized a...

Regional cortical thinning and area reduction are associated with cognitive impairment in hemodialysis patients.

Brain research bulletin
Magnetic resonance imaging (MRI) has shown that patients with end-stage renal disease have decreased gray matter volume and density. However, the cortical area and thickness in patients on hemodialysis are uncertain, and the relationship between pati...

Artificial Intelligence-Based Detection of Central Retinal Artery Occlusion Within 4.5 Hours on Standard Fundus Photographs.

Journal of the American Heart Association
BACKGROUND: Prompt diagnosis of acute central retinal artery occlusion (CRAO) is crucial for therapeutic management and stroke prevention. However, most stroke centers lack onsite ophthalmic expertise before considering fibrinolytic treatment. This s...

Radiomic 'Stress Test': exploration of a deep learning radiomic model in a high-risk prospective lung nodule cohort.

BMJ open respiratory research
BACKGROUND: Indeterminate pulmonary nodules (IPNs) are commonly biopsied to ascertain a diagnosis of lung cancer, but many are ultimately benign. The Lung Cancer Prediction (LCP) score is a commercially available deep learning radiomic model with str...