Ophthalmology

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41 papers4 papers in the last four weeks, level with the four weeks before. 0.0% of all new papers.

Towards Comprehensive Real-Time Scene Understanding in Ophthalmic Surgery through Multimodal Image Fusion
Mar 26, 2026Nikolo Rohrmoser, Ghazal Ghazaei, Michael Sommersperger +1Surgical Instrument Tracking Medical Image Analysis
Chair for Computer Aided Medical Procedures, Technical University of Munich, Garching, Germany. · Corporate Research and Technology, Carl Zeiss AG, Munich, Germany.
VOLMO: Versatile and Open Large Models for Ophthalmology
Mar 25, 2026Zhenyue Qin, Younjoon Chung, Elijah Lee +16Medical VLMs Medical LLMs
Department of Biomedical Informatics & Data Science, Yale University · Ray and Stephanie Lane Computational Biology Department, Carnegie Mellon University · Yong Loo Lin School of Medicine, National University of Singapore +4
Guideline-grounded retrieval-augmented generation for ophthalmic clinical decision support
Mar 23, 2026Shuying Chen, Sen Cui, Zhong CaoMultimodal RAG Retrieval-Augmented Generation
School of Artificial Intelligence and Data Science University of International Business and Economics Beijing, China · Tsinghua University Beijing, China · Heidelberg Institute of Global Health, Faculty of Medicine and University Hospital Heidelberg University Heidelberg, Germany
EyeWorld: A Generative World Model of Ocular State and Dynamics
Mar 14, 2026Ziyu Gao, Xinyuan Wu, Xiaolan Chen +10Ophthalmic Image Analysis Medical Image Analysis
School of Optometry, The Hong Kong Polytechnic University, Hong Kong. · Eye Center of Second Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, 310009, Zhejiang, China · Zhejiang Provincial Key Laboratory of Ophthalmology, Zhejiang Provincial Clinical Research Center for Eye Diseases, Zhejiang Provincial Engineering Institute On Eye Diseases, Hangzhou, 310009, Zhejiang, China +5
GLEAM: A Multimodal Imaging Dataset and HAMM for Glaucoma Classification
Mar 13, 2026Jiao Wang, Chi Liu, Yiying Zhang +8Glaucoma Detection Ophthalmic Image Analysis
College of the Information Science and Engineering, Northeastern University, Shenyang 110819, China · Department of Ophthalmology, Shenyang Fourth People’s Hospital, Shenyang 110000, China
Deep Expert Injection for Anchoring Retinal VLMs with Domain-Specific Knowledge
Mar 7, 2026Shuai Lu, Meng Wang, Jia Guo +6Vision-Language Models Medical VLMs
Beijing Institute of Technology, Beijing, China · Institute of High Performance Computing, Agency for Science, Technology and Research, Singapore · National University of Singapore, Singapore +2
Ordinal Diffusion Models for Color Fundus Images
Feb 27, 2026Gustav Schmidt, Philipp Berens, Sarah MüllerDiffusion-Based Image Generation Diffusion Models
Hertie Institute for AI in Brain Health, University of Tübingen, Germany
SKINOPATHY AI: Smartphone-Based Ophthalmic Screening and Longitudinal Tracking Using Lightweight Computer Vision
Feb 26, 2026S. Kalaycioglu, C. Hong, M. Zhu +1Ophthalmology Ophthalmic Image Analysis
Toronto Metropolitan University · York University · AIMechatroniX Inc. +3
retinalysis-vascx: An explainable software toolbox for the extraction of retinal vascular biomarkers
Feb 9, 2026Jose D. Vargas Quiros, Michael J. Beyeler, Sofia Ortin Vela +5Medical Image Analysis Fundus Imaging
Department of Ophthalmology, Erasmus University Medical Center, Rotterdam, the Netherlands · Department of Epidemiology, Erasmus University Medical Center, Rotterdam, the Netherlands · 5Dept. of Computational Biology, University of Lausanne, Lausanne, Switzerland +4
Bridging the Gap in Ophthalmic AI: MM-Retinal-Reason Dataset and OphthaReason Model toward Dynamic Multimodal Reasoning
Aug 22, 2025Ruiqi Wu, Yuang Yao, Na Su +11RL for VLMs Medical Image Analysis
School of Computer Science and Engineering, Southeast University, Nanjing, China · Department of Ophthalmology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China · School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, China
Clinician-Friendly Foundation Models for Ophthalmic Image Diagnostics without Fine-Tuning or Technical Barriers
Apr 22, 2025Meng Wang, Tian Lin, Qingshan Hou +69Medical Imaging Foundation Models Ophthalmic Image Analysis

Mar 26, 2026J/K move · Enter open · S save

Towards Comprehensive Real-Time Scene Understanding in Ophthalmic Surgery through Multimodal Image Fusion

Nikolo Rohrmoser, Ghazal Ghazaei, Michael Sommersperger, Nassir Navab

Chair for Computer Aided Medical Procedures, Technical University of Munich, Garching, Germany. · Corporate Research and Technology, Carl Zeiss AG, Munich, Germany.

Surgical Instrument Tracking Medical Image Analysis Ophthalmic Image Analysis Surgical Video Understanding Ophthalmology Multi-Source Image Fusion

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Purpose: The integration of multimodal imaging into operating rooms paves the way for comprehensive surgical scene understanding. In ophthalmic surgery, by now, two complementary imaging modalities are available: operating microscope (OPMI) imaging and real-time intraoperative optical coherence tomography (iOCT). This first work toward temporal OPMI and iOCT feature fusion demonstrates the potential of multimodal image processing for multi-head prediction through the example of precise instrument tracking in vitreoretinal surgery. Methods: We propose a multimodal, temporal, real-time capable network architecture to perform joint instrument detection, keypoint localization, and tool-tissue distance estimation. Our network design integrates a cross-attention fusion module to merge OPMI and iOCT image features, which are efficiently extracted via a YoloNAS and a CNN encoder, respectively. Furthermore, a region-based recurrent module leverages temporal coherence. Results: Our experiments demonstrate reliable instrument localization and keypoint detection (95.79% mAP50) and show that the incorporation of iOCT significantly improves tool-tissue distance estimation, while achieving real-time processing rates of 22.5 ms per frame. Especially for close distances to the retina (below 1 mm), the distance estimation accuracy improved from 284

μm

(OPMI only) to 33

μm

(multimodal). Conclusion: Feature fusion of multimodal imaging can enhance multi-task prediction accuracy compared to single-modality processing and real-time processing performance can be achieved through tailored network design. While our results demonstrate the potential of multi-modal processing for image-guided vitreoretinal surgery, they also underline key challenges that motivate future research toward more reliable, consistent, and comprehensive surgical scene understanding.