Ophthalmology

Refractive Surgery

Latest AI and machine learning research in refractive surgery for healthcare professionals.

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Machine-learning based classification of 2D-IR liquid biopsies enables stratification of melanoma relapse risk.

Non-linear laser spectroscopy methods such as two-dimensional infrared (2D-IR) produce large, inform...

Effect of Deep Learning-Based Image Reconstruction on Lesion Conspicuity of Liver Metastases in Pre- and Post-contrast Enhanced Computed Tomography.

The purpose of this study was to investigate the utility of deep learning image reconstruction at me...

A Clinical Neuroimaging Platform for Rapid, Automated Lesion Detection and Personalized Post-Stroke Outcome Prediction.

Predicting long-term functional outcomes for individuals with stroke is a significant challenge. Sol...

Artificial intelligence system improves the quality of digestive endoscopy: A prospective pretest and post-test single-center clinical trial.

BACKGROUND: With the assistance of ENDOANGEL, a study was conducted at Hainan General Hospital to ev...

Development and Validation of Machine Learning Algorithms for Predicting Prolonged Postoperative Opioid Use in Spinal Metastatic Disease.

Introduction: Operative management of spinal metastatic disease is largely for symptom palliation an...

Accuracy of Machine Learning in Predicting Post-Stroke Depression: A Systematic Review and Meta-Analysis.

INTRODUCTION: Post-stroke depression is one of the important complications of stroke and affects pat...

The effects of the post-delay epochs on working memory error reduction.

Accurate retrieval of the maintained information is crucial for working memory. This process primari...

An inherently interpretable AI model improves screening speed and accuracy for early diabetic retinopathy.

Diabetic retinopathy (DR) is a frequent complication of diabetes, affecting millions worldwide. Scre...

Identifying individuals at risk of post-stroke depression: Development and validation of a predictive model.

OBJECTIVES: To identify the factors associated with post-stroke depression (PSD) and develop a machi...

Machine Learning Assisted Stroke Prediction in Mechanical Circulatory Support: Predictive Role of Systemic Mitochondrial Dysfunction.

Stroke continues to be a major adverse event in advanced congestive heart failure (CHF) patients aft...

Mapping intellectual structure and research hotspots of cancer studies in primary health care: A machine-learning-based analysis.

In the contemporary fight against cancer, primary health care (PHC) services hold a significant and ...

Post-composing ontology terms for efficient phenotyping in plant breeding.

Ontologies are widely used in databases to standardize data, improving data quality, integration, an...

Feasibility of a Machine Learning Classifier for Predicting Post-Induction Hypotension in Non-Cardiac Surgery.

PURPOSE: To develop a machine learning (ML) classifier for predicting post-induction hypotension (PI...

Post-Training Network Compression for 3D Medical Image Segmentation: Reducing Computational Efforts via Tucker Decomposition.

Purpose To investigate whether the computational effort of three-dimensional CT-based multiorgan seg...

Artificial Intelligence in Predicting Ocular Hypertension After Descemet Membrane Endothelial Keratoplasty.

PURPOSE: Descemet membrane endothelial keratoplasty (DMEK) has emerged as a novel approach in cornea...

Post-Transplant Liver Monitoring Utilizing Integrated Surface-Enhanced Raman and AI in Hepatic Ischemia-Reperfusion Injury Animal Model.

BACKGROUND: While liver transplantation saves lives from irreversible liver damage, it poses challen...

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