Latest AI and machine learning research in laser surgery for healthcare professionals.
Objective: In this review, we aim to provide an analysis of current cardiac ablation techniques, such as radiofrequency ablation (RF), cryoablation, and pulsed-field ablation (PFA), with a focus on the role of computational modeling in enhancing the precision, safety, and effectiveness of these treatments. Particular attention is given to thermal management, exploring how computational approache...
Glaucoma is an incurable ophthalmic disease that damages the optic nerve, leads to vision loss, and ranks among the leading causes of blindness worldwide. Diagnosing glaucoma typically involves fundus photography, optical coherence tomography (OCT), and visual field testing. However, the high cost of OCT often leads to reliance on fundus photography and visual field testing, both of which exhibi...
In recent years, large language models (LLMs) have demonstrated remarkable potential across various medical applications. Building on this foundatio...
BACKGROUND: Accurate prediction of early recurrence (ER) is essential to improve the prognosis of patients with hepatocellular carcinoma (HCC) underwe...
Objective: Interventional devices, catheters and insertable imaging devices such as transesophageal echo (TOE) probes are routinely used in minimall...
This paper discusses how ophthalmologists often rely on multimodal data to improve diagnostic accuracy. However, complete multimodal data is rare in...
In the immunohistochemical (IHC) analysis during surgery, frozen-section (FS) images are used to determine the benignity or malignancy of the tumor....
Surgical phase recognition from video enables various downstream applications. Transformer-based sliding window approaches have set the state-of-the...
The training of deep learning models relies on a large amount of labeled data. However, the high cost of medical labeling seriously hinders the deve...
Glaucomatous optic neuropathy (GON) is a prevalent ocular disease that can lead to irreversible vision loss if not detected early and treated. The t...
The vascular structure in retinal images plays a crucial role in ophthalmic diagnostics, and its accuracies are directly influenced by the quality o...
Ophthalmologists typically require multimodal data sources to improve diagnostic accuracy in clinical decisions. However, due to medical device shor...
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 huma...
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...