Latest AI and machine learning research in laser surgery for healthcare professionals.
Ophthalmic diseases pose a significant global health challenge, yet traditional diagnosis methods and existing single-eye deep learning approaches often fail to account for binocular pathological correlations. To address this, we propose DMS-Net, a dual-modal multi-scale Siamese network for binocular fundus image classification. Our framework leverages weight-shared Siamese ResNet-152 backbones ...
Artificial intelligence (AI) shows remarkable potential in medical imaging diagnostics, yet most current models require retraining when applied across different clinical settings, limiting their scalability. We introduce GlobeReady, a clinician-friendly AI platform that enables fundus disease diagnosis that operates without retraining, fine-tuning, or the needs for technical expertise. GlobeRead...
Medical Large Vision-Language Models (Med-LVLMs) demonstrate significant potential in healthcare, but their reliance on general medical data and coa...
Soft robots exhibit inherent compliance and safety, which makes them particularly suitable for applications requiring direct physical interaction wi...
Accurate retinal vessel segmentation provides essential structural information for ophthalmic image analysis. However, existing methods struggle wit...
The growing burden of myopia and retinal diseases necessitates more accessible and efficient eye screening solutions. This study presents a compact,...
Accurate measurement of eyelid parameters such as Margin Reflex Distances (MRD1, MRD2) and Levator Function (LF) is critical in oculoplastic diagnos...
With the advancements in medical artificial intelligence (AI), fundus image classifiers are increasingly being applied to assist in ophthalmic diagn...
Large language models (LLMs) are increasingly used in healthcare, but their reliability is heavily influenced by user-driven factors such as questio...
Objective: In this review, we aim to provide an analysis of current cardiac ablation techniques, such as radiofrequency ablation (RF), cryoablation,...
Glaucoma is an incurable ophthalmic disease that damages the optic nerve, leads to vision loss, and ranks among the leading causes of blindness worl...
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...