Laser Surgery

Advancements and Prospects in Nanorobotic Applications for Ophthalmic Therapy.

This study provides a bibliometric and bibliographic review of emerging applications of micro- and nanotechnology in treating ocular diseases, with a primary focus on glaucoma. We aim to identify key research trends and analyze advancements in devices and drug delivery systems for ocular treatments. The methodology involved analyzing 385 documents indexed on the Web of Science using tools such as ...

An effective vessel segmentation method using SLOA-HGC.

Accurate segmentation of retinal blood vessels from retinal images is crucial for detecting and diagnosing a wide range of ophthalmic diseases. Our retinal blood vessel segmentation algorithm enhances microfine vessel extraction, improves edge texture clarity, and normalizes vessel distribution. It stabilizes neural network training for complex retinal vascular features. Channel-aware self-attenti...

Ocular Biometry OCR: a machine learning algorithm leveraging optical character recognition to extract intra ocular lens biometry measurements.

Given close relationships between ocular structure and ophthalmic disease, ocular biometry measurements (including axial length, lens thickness, anter...

Machine learning derived retinal pigment score from ophthalmic imaging shows ethnicity is not biology.

Few metrics exist to describe phenotypic diversity within ophthalmic imaging datasets, with researchers often using ethnicity as a surrogate marker fo...

Estimating Visual Acuity With Spectacle Correction From Fundus Photos Using Artificial Intelligence.

IMPORTANCE: Determining spectacle-corrected visual acuity (VA) is essential when managing many ophthalmic diseases. If artificial intelligence (AI) ev...

Sub-millimeter fiberscopic robot with integrated maneuvering, imaging, and biomedical operation abilities.

Small-scale continuum robots hold promise for interventional diagnosis and treatment, yet existing models struggle to achieve small size, precise stee...

Predicting lack of clinical improvement following varicose vein ablation using machine learning.

OBJECTIVE: Varicose vein ablation is generally indicated in patients with active/healed venous ulcers. However, patient selection for intervention in ...

12 lead surface ECGs as a surrogate of atrial electrical remodeling - a deep learning based approach.

BACKGROUND AND PURPOSE: Atrial fibrillation (AF), a common arrhythmia, is linked with atrial electrical and structural changes, notably low voltage ar...

Machine Learning-Driven Identification of Distinct Persistent Atrial Fibrillation Phenotypes: A Cluster Analysis of DECAAF II.

INTRODUCTION: Catheter ablation of persistent atrial fibrillation yields sub-optimal success rates partly due to the considerable heterogeneity within...

The Role of Artificial Intelligence in Epiretinal Membrane Care: A Scoping Review.

TOPIC: In ophthalmology, artificial intelligence (AI) demonstrates potential in using ophthalmic imaging across diverse diseases, often matching ophth...

Multimodal MRI radiomics-based stacking ensemble learning model with automatic segmentation for prognostic prediction of HIFU ablation of uterine fibroids: a multicenter study.

OBJECTIVES: To evaluate the effectiveness of an MRI radiomics stacking ensemble learning model, which combines T2-weighted imaging (T2WI) and contrast...

Predicting Early recurrence of atrial fibrilation post-catheter ablation using machine learning techniques.

BACKGROUND: Catheter ablation is a common treatment for atrial fibrillation (AF), but recurrence rates remain variable. Predicting the success of cath...

Editorial: Efficient artificial intelligence (AI) in ophthalmic imaging.

Artificial intelligence and glaucoma: a lucid and comprehensive review.

Glaucoma is a pathologically irreversible eye illness in the realm of ophthalmic diseases. Because it is difficult to detect concealed and non-obvious...

Self-improving generative foundation model for synthetic medical image generation and clinical applications.

In many clinical and research settings, the scarcity of high-quality medical imaging datasets has hampered the potential of artificial intelligence (A...

Editorial: The evolution of minimally invasive urologic surgery: innovations, challenges, and opportunities.

Utility of a Large Language Model for Extraction of Clinical Findings from Healthcare Data following Lung Ablation: A Feasibility Study.

To assess the feasibility of utilizing a large language model (LLM) in extracting clinically relevant information from healthcare data in patients who...

Assessing the Efficacy and Clinical Utility of Artificial Intelligence Scribes in Urology.

OBJECTIVE: To assess the quality of artificial intelligence (AI) scribes and to evaluate their impact on urologic practice.

Predicting Survival and Recurrence of Lung Ablation Patients Using Deep Learning-Based Automatic Segmentation and Radiomics Analysis.

PURPOSE: To predict survival and tumor recurrence following image-guided thermal ablation (IGTA) of lung tumors segmented using a deep learning approa...

Predicting the cognitive impairment with multimodal ophthalmic imaging and artificial neural network for community screening.

BACKGROUND/AIMS: To investigate the comprehensive prediction ability for cognitive impairment in a general elder population using the combination of t...