Ophthalmology

An integrated language-vision foundation model for conversational diagnostics and triaging in primary eye care

Current deep learning models are mostly task specific and lack a user-friendly interface to operate. We present Meta-EyeFM, a multi-function foundation model that integrates a large language model (LLM) with vision foundation models (VFMs) for ocular disease assessment. Meta-EyeFM leverages a routing mechanism to enable accurate task-specific analysis based on text queries. Using Low Rank Adapta...

Bppv nystagmus signals diagnosis framework based on deep learning.

Benign Paroxysmal Positional Vertigo (BPPV) is a prevalent vestibular disorder encountered in clinical settings. Diagnosis of this condition primarily relies on the observation of nystagmus, which involves monitoring the eye movements of patients. However, existing medical equipment for collecting and analyzing nystagmus data has notable limitations and deficiencies. To address this challenge, a c...

Insights into KMT2A rearrangements in acute myeloid leukemia: from molecular characteristics to targeted therapies.

Acute myeloid leukemia (AML) with KMT2A rearrangements (KMT2A-r) represents a highly aggressive and prognostically unfavorable subtype of leukemia, of...

Text-Assisted Vision Model for Medical Image Segmentation.

Precise medical image segmentation is important for automating diagnosis and treatment planning in healthcare. While images present the most significa...

Vitreous proteomics in rhegmatogenous retinal detachment and proliferative vitreoretinopathy.

Rhegmatogenous retinal detachment (RRD) is a serious ophthalmic condition that, if untreated, can result in significant vision loss. Proliferative vit...

Automated Early-Stage Glaucoma Detection Using a Robust Concatenated AI Model.

Glaucoma is a leading cause of irreversible blindness worldwide; therefore, detection of this disease in its early stage is crucial. However, previous...

Diagnostic Performance of Publicly Available Large Language Models in Corneal Diseases: A Comparison with Human Specialists.

This study evaluated the diagnostic accuracy of seven publicly available large language models (LLMs)-GPT-3.5, GPT-4.o Mini, GPT-4.o, Gemini 1.5 Flas...

Detection of Systemic Diseases From Ocular Images Using Artificial Intelligence: A Systematic Review.

PURPOSE: Despite the huge investment in health care, there is still a lack of precise and easily accessible screening systems. With proven association...

Interpreting Deep Learning Studies in Glaucoma: Unresolved Challenges.

Deep learning algorithms as tools for automated image classification have recently experienced rapid growth in imaging-dependent medical specialties, ...

Ethics of Artificial Intelligence in Medicine and Ophthalmology.

BACKGROUND: This review explores the bioethical implementation of artificial intelligence (AI) in medicine and in ophthalmology. AI, which was first i...

A Compact Surgical Robot System for Craniomaxillofacial Surgery and its Preliminary Study.

Craniomaxillofacial surgery has the characteristics of complex anatomical structure, narrow surgical field, and easy damage to nerves, blood vessels, ...

Vision Foundation Model Embedding-Based Semantic Anomaly Detection

Semantic anomalies are contextually invalid or unusual combinations of familiar visual elements that can cause undefined behavior and failures in sy...

Deep Learning Advances in Vision-Based Traffic Accident Anticipation: A Comprehensive Review of Methods,Datasets,and Future Directions

Traffic accident prediction and detection are critical for enhancing road safety,and vision-based traffic accident anticipation (Vision-TAA) has eme...

Selftok: Discrete Visual Tokens of Autoregression, by Diffusion, and for Reasoning

We completely discard the conventional spatial prior in image representation and introduce a novel discrete visual tokenizer: Self-consistency Token...

Ophora: A Large-Scale Data-Driven Text-Guided Ophthalmic Surgical Video Generation Model

In ophthalmic surgery, developing an AI system capable of interpreting surgical videos and predicting subsequent operations requires numerous ophtha...

UAV-CodeAgents: Scalable UAV Mission Planning via Multi-Agent ReAct and Vision-Language Reasoning

We present UAV-CodeAgents, a scalable multi-agent framework for autonomous UAV mission generation, built on large language and vision-language model...

EmoVLM-KD: Fusing Distilled Expertise with Vision-Language Models for Visual Emotion Analysis

Visual emotion analysis, which has gained considerable attention in the field of affective computing, aims to predict the dominant emotions conveyed...

Evaluating Vision and Pathology Foundation Models for Computational Pathology: A Comprehensive Benchmark Study.

To advance precision medicine in pathology, robust AI-driven foundation models are increasingly needed to uncover complex patterns in large-scale path...

[Construction of an interpretable machine learning-based prediction model for the clinical effect on ischemic stroke in treatment with eye acupuncture combined with rehabilitation therapy].

OBJECTIVE: To construct a prediction model for the clinical effect of eye acupuncture combined with rehabilitation therapy on ischemic stroke based on...

Integrating Bayesian and neural networks models for eye movement prediction in hybrid search.

Visual search is crucial in daily human interaction with the environment. Hybrid search extends this by requiring observers to find any item from a gi...