AIMC Topic: Male

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Machine learning-based prediction of stone-free rate after retrograde intrarenal surgery for lower pole renal stones.

World journal of urology
BACKGROUND: Lower pole renal stones (LPS) present unique challenges for retrograde intrarenal surgery (RIRS) due to unfavorable anatomical features, often resulting in suboptimal stone-free rates (SFR). Recent advancements in machine learning (ML) of...

Cost-effectiveness analysis of artificial intelligence (AI) in earlier detection of liver lesions in cirrhotic patients at risk of hepatocellular carcinoma in Italy.

Journal of medical economics
BACKGROUND: Hepatocellular carcinoma (HCC) is the fifth most common cancer worldwide and the third most common cause of cancer-related death. Cirrhosis is a major contributing factor, accounting for over 90% of HCC cases. With the high mortality rate...

Epigenetic signatures of accelerated aging and immune dysregulation in youth with social anxiety disorder.

Behavioural brain research
Social anxiety disorder (SAD) is a prevalent psychiatric condition with significant psychological and socioeconomic consequences. While its psychological characteristics are well-documented, the underlying molecular mechanisms remain poorly understoo...

Clinical predictors of treatment resistant depression.

European neuropsychopharmacology : the journal of the European College of Neuropsychopharmacology
Despite advances in the treatment of major depressive disorder (MDD) yet a substantial proportion of patients fail to achieve remission and instead develop treatment-resistant depression (TRD). Identifying robust clinical predictors of response is es...

Fusion of bio-inspired optimization and machine learning for Alzheimer's biomarker analysis.

Computers in biology and medicine
Identification of Alzheimer's Disease (AD), especially in its early phases, presents significant challenges due to the nonexistence of reliable biomarkers and effective treatments. Clinical trials for AD medications also suffer from high failure rate...

EEG quantization and entropy of multi-step transition probabilities for driver drowsiness detection via LSTM.

Computers in biology and medicine
Detecting driver drowsiness through electroencephalogram (EEG) poses challenges due to the complexity and variability of brain activity across different subjects. This study proposes a feature extraction pipeline combined with a Long Short-Term Memor...

Letter to the Editor: Robustness of osteoporosis risk prediction models with enhanced statistical analyses.

Computers in biology and medicine
In response to Oka et al.'s letter, we conducted additional statistical analyses to validate the robustness of our osteoporosis risk prediction model using NHANES 2007-2014 data (n = 7924). We evaluated 10 key predictors through Spearman's rho, Kenda...

Hierarchical deep learning system for orbital fracture detection and trap-door classification on CT images.

Computers in biology and medicine
OBJECTIVE: To develop and evaluate a hierarchical deep learning system that detects orbital fractures on computed tomography (CT) images and classifies them as depressed or trap-door types.

Enhancing Parkinson's disease prediction using meta-heuristic optimized machine learning models.

Personalized medicine
Parkinson's disease is a progressive neurological disorder affecting movement and cognition. Early detection is crucial but challenging with traditional methods. This study applies meta-heuristic optimization to enhance machine learning prediction mo...

Prediction of Percutaneous Coronary Intervention Success in Patients With Moderate to Severe Coronary Artery Calcification Using Machine Learning Based on Coronary Angiography: Prospective Cohort Study.

Journal of medical Internet research
BACKGROUND: Given the challenges faced during percutaneous coronary intervention (PCI) for heavily calcified lesions, accurately predicting PCI success is crucial for enhancing patient outcomes and optimizing procedural strategies.