Laser Surgery

A hybrid CNN and reinforcement learning framework for speaker identification using Mel-Spectrogram and continuous wavelet transform features.

Speaker identification remains critical in biometric authentication systems, requiring robust feature extraction strategies that capture speaker-specific vocal characteristics. This study introduces a hybrid deep learning architecture integrating Convolutional Neural Networks (CNNs) with Reinforcement Learning (RL) for confidence-aware speaker identification. Two feature extraction methodologies w...

6G conditioned spatiotemporal graph neural networks for real time traffic flow prediction.

Accurate, low-latency traffic forecasting is a cornerstone capability for next-generation Intelligent Transportation Systems (ITS). This paper investigates how emerging 6G-era network context specifically per node slice-bandwidth and channel-quality indicators can be fused with spatio-temporal graph models to improve short-term freeway speed prediction while respecting strict real-time constraints...

Evolving strategies may facilitate oral pathology and cancer surveillance, monitoring, and detection for patients at risk.

BACKGROUND: Oral cancers account for considerable morbidity and mortality worldwide, and their incidence has increased in the past several years, desp...

Therapeutic nanofibrous scaffolds in precision drug delivery: formulation design, challenges and regulatory frameworks.

INTRODUCTION: Drug-loaded nanofibrous systems represent a breakthrough in drug delivery, overcoming limitations of conventional formulations. They dem...

[Enhancement of radiomics-based machine learning models for predicting efficacy of high-intensity focused ultrasound ablation of uterine fibroids using undersampling methods].

OBJECTIVES: To improve the accuracy of machine learning models for preoperative prediction of high-intensity focused ultrasound (HIFU) ablation effica...

Preoperative Neuroimaging Markers, Clinical Severity Measures, and Shunt Characteristics for Predicting Shunt Revision in Idiopathic Intracranial Hypertension: An Explainable Machine-Learning Study.

BACKGROUND AND PURPOSE: Surgical shunt placement is a common treatment for idiopathic intracranial hypertension (IIH) but is hampered by high revision...

FetCAT: Cross-attention fusion of transformer-CNN architecture for fetal brain plane classification with explainability using motion-degraded MRI.

Fetal brain magnetic resonance imaging (MRI) has been recognized as a vital diagnostic tool for identifying neurological anomalies during pregnancy. A...

Resolution of generalized tonic seizures following focal ablative or resective surgery.

OBJECTIVE: Focal brain lesions may underlie generalized tonic seizures, as seen in Lennox-Gastaut syndrome, by engaging bilateral neural networks. How...

Using Latent Dirichlet Allocation Topic Modeling to Uncover Latent Research Topics and Trends in Renal Cell Carcinoma: Bibliometric Review.

BACKGROUND: Renal cell carcinoma (RCC) is a common, often lethal kidney cancer that originates in the renal cortex. Its incidence is rising, and major...

Atrial Fibrillation Ablation Using 3D Artificial Intelligence Module Integration with Intracardiac Echocardiography.

BACKGROUND: Intracardiac echocardiography (ICE)-based electroanatomical mapping (EAM) improves procedural efficiency and safety in atrial fibrillation...

Quasi-multimodal-based pathophysiological feature learning for retinal disease diagnosis.

Retinal diseases spanning a broad spectrum can be effectively identified and diagnosed using complementary signals from multimodal data. However, mult...

A Review of Recent Developments in Artificial Intelligence and Big Data Technologies for Ophthalmology Referrals and Clinical Practice.

Ophthalmology is undergoing rapid transformation through the integration of smart technologies such as artificial intelligence (AI), big data analytic...

Biologically explicable multimodal model predicting local tumor progression and tumor invasiveness of hepatocellular carcinoma.

PURPOSE: Local tumor progression (LTP) of hepatocellular carcinoma (HCC) after thermal ablation (TA) is related to tumor invasiveness and threaten the...

Comparative study of 11 machine learning algorithms for predicting recurrence risk after atrial fibrillation catheter ablation based on a real-world cohort: a retrospective study.

BACKGROUND: Atrial fibrillation (AF) is the most common arrhythmia worldwide, with catheter ablation being an effective yet recurrence-prone treatment...

Machine learning for the prediction of atrial fibrillation recurrence after catheter ablation: A systematic review and meta-analysis.

BACKGROUND AND OBJECTIVE: This systematic review evaluates the current state of Machine Learning (ML) methods for predicting Atrial Fibrillation (AF) ...

An Ensemble Learning Artificial Intelligence Model for Alzheimer's Disease Detection Using OCT.

PURPOSE: There has been significant progress in detecting Alzheimer's disease (AD) using retinal imaging. We developed an ensemble learning-based deep...

From bench to bedside: The clinical relevance of atrial electrical remodeling in atrial fibrillation therapy.

Atrial electrical remodeling spans molecular, electrical, and structural alterations that shorten refractoriness, facilitate reentry, and ultimately c...

The role of focal therapy for localized prostate cancer: From diagnosis to ablation.

INTRODUCTION: Focal therapy (FT) has emerged as an intermediate therapeutic strategy between active surveillance (AS) and radical treatments for the m...

Magnetically Driven Lasing Microrobots for Precise Photodynamic Therapy.

Photodynamic therapy (PDT) is an emerging approach for tumor treatment, valued for its noninvasive and stimuli-responsive properties. However, its the...

Leveraging Interpretable AI for Deciphering Signature Histopathologic Patterns.

BACKGROUND: Leukocytoclastic vasculitis (LCV) and microvascular occlusion (MVO) are distinct histopathologic patterns underlying dermatologic diagnose...