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Bridging Vision and Text: Applications and Challenges of Vision-Language Models in Urological Surgery

European urology Focus 2025年

作者： Bogaert, Wouter Carl, Nicolas Kowalewski, Karl-Friedrich Michel, Maurice Stephan Mottrie, Alexandre De Backer, Pieter ORSI Academy Melle Belgium Department of Electronics and Information Systems University of Ghent Ghent Belgium Department of Urology University Medical Centre Mannheim University of Heidelberg Mannheim Germany Division of Intelligent Systems and Robotics in Urology German Cancer Research Center Heidelberg Heidelberg Germany DKFZ Hector Cancer Institute University Medical Center Mannheim Mannheim Germany Department of Urology AZORG Hospital Aalst Belgium Department Human Structure and Repair University of Ghent Ghent Belgium

Vision-language models (VLMs) integrate visual data, such as surgical videos and medical images, with textual information for advanced artificial intelligence (AI) capabilities in surgery. This mini review highlights recent developments in the application of VLMs to surgical tasks in urology, such as answering clinical questions about surgical images, recognizing surgical instruments, identifying surgical phases, and detecting errors during procedures. Despite the potential of VLMs, significant challenges remain, particularly the limited availability of high-quality data sets. Future progress depends on overcoming these limitations, enhancing the robustness and reliability of VLMs, and creating standardized data sets. Ultimately, VLMs represent a promising advance towards integrated, multimodal AI systems capable of supporting surgeons via automated guidance, educational support, and performance evaluation. Patient summary: Our mini review explores new artificial intelligence (AI) tools that combine visual images and text to assist surgeons during operations. These AI tools can recognize instruments, identify surgical phases, and answer questions about surgery. Improved versions could help in making surgery safer and more efficient in the future. © 2025 European Association of urology

关键词： Robot-assisted surgery Vision-language model

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CARL: Camera-Agnostic Representation Learning for Spectral Image Analysis

arXiv

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arXiv 2025年

作者： Baumann, Alexander Ayala, Leonardo Seidlitz, Silvia Sellner, Jan Studier-Fischer, Alexander Özdemir, Berkin Maier-Hein, Lena Ilic, Slobodan Siemens AG Munich Germany Heidelberg Germany Medical Faculty Faculty of Mathematics and Computer Science Heidelberg University Germany NCT Heidelberg Germany HIDSS4Health Heidelberg Germany Department of Urology and Urosurgery University Medical Center Mannheim Germany Department of General Visceral and Transplantation Surgery Heidelberg University Hospital Germany Division of Intelligent Systems and Robotics in Urology DKFZ Heidelberg Germany DKFZ Hector Cancer Institute University Medical Center Mannheim Germany

Spectral imaging offers promising applications across diverse domains, including medicine and urban scene understanding, and is already established as a critical modality in remote sensing. However, variability in channel dimensionality and captured wavelengths among spectral cameras impede the development of AI-driven methodologies, leading to camera-specific models with limited generalizability and inadequate cross-camera applicability. To address this bottleneck, we introduce CARL, a model for Camera-Agnostic Representation Learning across RGB, multispectral, and hyperspectral imaging modalities. To enable the conversion of a spectral image with any channel dimensionality to a camera-agnostic embedding, we introduce wavelength positional encoding and a self-attention-cross-attention mechanism to compress spectral information into learned query representations. Spectral-spatial pre-training is achieved with a novel spectral self-supervised JEPA-inspired strategy tailored to CARL. Large-scale experiments across the domains of medical imaging, autonomous driving, and satellite imaging demonstrate our model’s unique robustness to spectral heterogeneity, outperforming on datasets with simulated and real-world cross-camera spectral variations. The scalability and versatility of the proposed approach position our model as a backbone for future spectral foundation models. © 2025, CC BY.

关键词： Image coding

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Correction: Comparison of distance versus in-person laparoscopy training using a low-cost laparoscopy simulator-a randomized controlled multi-center trial

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Surgical endoscopy 2024年第12期38卷 7689页

作者： Mark Enrik Geissler Jean-Paul Bereuter Rona Berit Geissler Guus Mattheus Johannes Bökkerink Luisa Egen Karl-Friedrich Kowalewski Caelan Haney Else Kroener Fresenius Center for Digital Health Faculty of Medicine and University Hospital Carl Gustav Carus TUD Dresden University of Technology 01307 Dresden Germany. mark_enrik.geissler@tu-dresden.de. Department of Urology and Urosurgery University Medical Centre Mannheim University of Heidelberg Mannheim Germany. mark_enrik.geissler@tu-dresden.de. Department of Urology and Urosurgery University Medical Centre Mannheim University of Heidelberg Mannheim Germany. Princess Máxima Center for Pediatric Oncology Princess Maxima Center Utrecht The Netherlands. Division Intelligent Systems and Robotics in Urology German Cancer Research Center (DKFZ) Heidelberg Germany. DKFZ Hector Cancer Institute at the University Medical Center Mannheim Germany. Department of Urology and Urosurgery University Medical Centre Mannheim University of Heidelberg Mannheim Germany. caelan.haney@dkfz-heidelberg.de. Division Intelligent Systems and Robotics in Urology German Cancer Research Center (DKFZ) Heidelberg Germany. caelan.haney@dkfz-heidelberg.de. DKFZ Hector Cancer Institute at the University Medical Center Mannheim Germany. caelan.haney@dkfz-heidelberg.de.

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