Laser Surgery

Large-scale observational study of AI-based patient and surgical material verification system in ophthalmology: real-world evaluation in 37 529 cases.

BACKGROUND: Surgical errors in ophthalmology can have devastating consequences. We developed an artificial intelligence (AI)-based surgical safety system to prevent errors in patient identification, surgical laterality and intraocular lens (IOL) selection. This study aimed to evaluate its effectiveness in real-world ophthalmic surgical settings.

Recent advances and future perspectives of long-acting ophthalmic preparations (LAOPs) in clinical applications.

The intricate structure of ocular barriers significantly impedes drug penetration, leading to suboptimal efficacy of conventional ophthalmic formulations. Sustained/controlled-release long-acting ophthalmic preparations (LAOPs) address these limitations by prolonging drug retention, reducing dosing frequency, and enhancing therapeutic precision. This review categorizes clinically validated LAOPs b...

Identifying Clinical Research Priorities in Interventional Pulmonary: An Interventional Pulmonology Outcomes Group (IPOG) Working Group Report.

The field of Interventional Pulmonology suffers from a paucity of methodologically robust studies to inform patient care, often relying on retrospecti...

The acceptance of ophthalmic artificial intelligence for eye diseases: a literature review and qualitative analysis.

Thorough investigations of end-users' awareness, acceptance, and concerns about ophthalmic artificial intelligence (AI) are essential to ensure its su...

Current status and challenges of minimally invasive ultrasound thermal ablation technology.

Image-guided minimally invasive ultrasound thermal ablation has been widely studied for disease treatment due to its unique advantages, such as large ...

Enhancing the Accuracy of Skin Lesion Diagnosis Using Hyperspectral Imaging and Deep Learning.

This study presents a novel diagnostic approach that integrates hyperspectral imaging (HSI) with deep learning to discriminate among dermatitis, actin...

Atrial Fibrillation Recurrence After Catheter Ablation: The Puzzle We Have and the Pieces We Need.

Atrial fibrillation (Afib) recurrence following catheter ablation (CA) remains a significant challenge within the electrophysiology community, potenti...

UNIK (Urologic Non-Neoplastic Investigation of Kidneys): a machine learning approach to decode benign lesion.

PURPOSE: Predicting the likelihood of benign neoplasia in patients with suspected renal cell carcinoma (RCC) is a cornerstone of presurgical planning....

Energy consumption analysis and prediction in exercise training based on accelerometer sensors and deep learning.

This study aims to enhance the accuracy and efficiency of energy consumption prediction during exercise training and address the limitations of existi...

Effectiveness of smartphone technology for detection of paediatric ocular diseases-a systematic review.

BACKGROUND: Artificial intelligence has become part of healthcare with a multitude of applications being customized to roles required in clinical prac...

Hybrid attention-based deep learning for multi-label ophthalmic disease detection on fundus images.

BACKGROUND: Ophthalmic diseases significantly impact vision and quality of life. Early diagnosis using fundus images is critical for timely treatment....

A scoping review of artificial intelligence as a medical device for ophthalmic image analysis in Europe, Australia and America.

This scoping review aims to identify regulator-approved ophthalmic image analysis artificial intelligence as a medical device (AIaMD) in three jurisdi...

Complex Fractionated Atrial Electrograms, Dispersion, and Voltage in Persistent Atrial Fibrillation: Implications for Electrogram-Guided Ablation.

BACKGROUND: Various methods have been used to identify substrate of persistent atrial fibrillation (PeAF) including complex fractionated atrial electr...

Diagnosis and management of Candida endophthalmitis and chorioretinitis.

BACKGROUND: Candida endophthalmitis (CE) and chorioretinitis are uncommon but potentially devastating complications of candidemia, associated with sig...

A Deep Learning-Based Multimodal Fusion Model for Recurrence Prediction in Persistent Atrial Fibrillation Patients.

BACKGROUND: The long-term success rate of atrial fibrillation (AF) ablation remains a significant clinical challenge, particularly in patients with pe...

Machine learning-driven prediction of cycloplegic refractive error in Chinese children.

OBJECTIVE: To develop and validate machine learning (ML) models for predicting cycloplegic spherical equivalent refraction (SER) using non-cycloplegic...

Ophthalmic Image Synthesis and Analysis with Generative Adversarial Network Artificial Intelligence.

Generative adversarial networks (GANs), introduced by Ian Goodfellow in 2014, have revolutionized adversarial machine learning, particularly in data s...

An Artificial Intelligence Method for Phenotyping of OCT-Derived Thickness Maps Using Unsupervised and Self-supervised Deep Learning.

The objective of this study is to enhance the understanding of ophthalmic disease physiology and genetic architecture through the analysis of optical ...

Synergistic AI-resident approach achieves superior diagnostic accuracy in tertiary ophthalmic care for glaucoma and retinal disease.

INTRODUCTION: Artificial intelligence (AI) shows promise in ophthalmology, but its potential in tertiary care settings in Latin America remains unders...

Nystagmus in Clinical Practice: From Diagnosis to Treatment-A Comprehensive Review.

Nystagmus, a common yet intricate ocular movement disorder, significantly contributes to visual morbidity in the paediatric and adult populations. Def...