Latest AI and machine learning research in laser surgery for healthcare professionals.
Ophthalmologists typically require multimodal data sources to improve diagnostic accuracy in clinical decisions. However, due to medical device shortages, low-quality data and data privacy concerns, missing data modalities are common in real-world scenarios. Existing deep learning methods tend to address it by learning an implicit latent subspace representation for different modality combination...
In this paper, we present a novel Left-Prompt-Guided (LPG) paradigm to address a diverse range of reference-based vision tasks. Inspired by the human creative process, we reformulate these tasks using a left-right stitching formulation to construct contextual input. Building upon this foundation, we propose AnyRefill, an extension of LeftRefill, that effectively adapts Text-to-Image (T2I) models...
With the continuous evolution of computer technology and the surging advent of the big data era, artificial intelligence (AI) has already manifested e...
Catheter ablation of Atrial Fibrillation (AF) consists of a one-size-fits-all treatment with limited success in persistent AF. This may be due to ou...
The hemorrhagic lesion segmentation plays a critical role in ophthalmic diagnosis, directly influencing early disease detection, treatment planning,...
Large language models (LLMs) have shown significant promise across various medical applications, with ophthalmology being a notable area of focus. M...
To ensure the quality of care for inpatients in ophthalmic hospitals, address the complex and variable conditions of postoperative patients, and condu...
Vision-language pretraining (VLP) has been investigated to generalize across diverse downstream tasks for fundus image analysis. Although recent met...
Retinal image registration is vital for diagnostic therapeutic applications within the field of ophthalmology. Existing public datasets, focusing on...
The introduction of optical coherence tomography (OCT) in the 1990s revolutionized diagnostic ophthalmic imaging. Initially, OCT's role was primarily ...
Cancer of unknown primary (CUP) remains a major diagnostic hurdle, compromising therapies that depend on accurately identifying tissue of origin. We p...
To compare the performance and cost-effectiveness of DeepSeek-R1 with OpenAI o1 in diagnosing and managing ophthalmology clinical cases. Cross-section...
The rapid advancement of AI in ophthalmology is transforming diagnostics, especially in resource-limited settings. The shortage of ophthalmologists an...
The choroid layer plays a critical role in maintaining outer retinal health and is implicated in numerous vision-threatening diseases such as diabetic...
To train and evaluate segmentation-free 3D convolutional neural network (3DCNN) models for estimating visual field (VF) from optical coherence tomogra...
This study aimed to evaluate the novelty and potential value of therapeutic suggestions made by an artificial intelligence large language model for tr...
Standard Automated Perimetry (SAP) is the mainstay for monitoring glaucoma progression and has been accepted by the U.S. Food and Drug Administration ...
To develop and validate an artificial intelligence (AI)-based model that automatically measures choroidal mass dimensions on B□scan ophthalmic ultraso...
Premature ventricular contractions (PVCs) are common in patients with and without structural heart disease. In a subset of patients, PVCs are associat...
Deep learning has shown promise in diabetic retinopathy screening using fundus images. However, many existing models operate as “black boxes,” providi...