Martin Wagner

Attending Surgeon and Full Professur of AI-Based Assistance Systems in Surgery

University Hospital Carl Gustav Carus Dresden

Germany

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Martin Wagner is an Attending Surgeon and Full Professor of AI-Based Assistance Systems in Surgery at the University Hospital Carl Gustav Carus at Technische Universität Dresden, Germany. His research focuses on the integration of artificial intelligence in surgical practices, emphasizing topics such as machine learning, preoperative complication risk stratification, and robotic assistance in surgery. Recent publications include studies on point cloud segmentation for laparoscopic procedures, the development of annotation-free computational tools, and the exploration of trust in AI among surgical professionals.

Martin Wagner

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Articles (24)

Aliado - A design concept of AI for decision support in oncological liver surgery

Assessment of the extent of lymphadenectomy in esophageal cancer surgery in the observational TIGER study

Point Cloud Segmentation for Autonomous Clip Positioning in Laparoscopic Cholecystectomy on a Phantom

AutoFRS: an externally validated, annotation-free approach to computational preoperative complication risk stratification in pancreatic surgery – an experimental study

“Who do you trust?” — levels of evidence in the era of artificial intelligence

Can surgeons trust AI? Perspectives on machine learning in surgery and the importance of eXplainable Artificial Intelligence (XAI)

Semi-Autonomous Robotic Assistance for Gallbladder Retraction in Surgery

Der digitale Operationssaal

Künstliche Intelligenz in der onkologischen Chirurgie

Comparing a virtual reality head-mounted display to on-screen three-dimensional visualization and two-dimensional computed tomography data for training in decision making in hepatic surgery

Introducing and Validating the Multiphasic Evidential Decision-Making Matrix (MedMax) for Clinical Management in Patients with Intrahepatic Cholangiocarcinoma

A surgical activity model of laparoscopic cholecystectomy for co-operation with collaborative robots

Toward an interoperable, intraoperative situation recognition system via process modeling, execution, and control using the standards BPMN and CMMN

SAGES consensus recommendations on surgical video data use, structure, and exploration (for research in artificial intelligence, clinical quality improvement, and surgical education)

A sensorized modular training platform to reduce vascular damage in endovascular surgery

Comparative validation of machine learning algorithms for surgical workflow and skill analysis with the HeiChole benchmark

Sim-to-Real Transfer for Visual Reinforcement Learning of Deformable Object Manipulation for Robot-Assisted Surgery

Sim-to-Real Transfer for Visual Reinforcement Learning of Deformable Object Manipulation for Robot-Assisted Surgery

Machine learning for optimized individual survival prediction in resectable upper gastrointestinal cancer

Active learning for extracting surgomic features in robot-assisted minimally invasive esophagectomy: a prospective annotation study

Ensuring privacy protection in the era of big laparoscopic video data: development and validation of an inside outside discrimination algorithm (IODA)

Surgomics: personalized prediction of morbidity, mortality and long-term outcome in surgery using machine learning on multimodal data

Surgomics: personalized prediction of morbidity, mortality and long-term outcome in surgery using machine learning on multimodal data

Interactive visual exploration of surgical process data

Collaborators (10)

Stefanie Speidel

Oliver Burgert

Wolfgang Reiter

George B. Hanna

Christian Marzi

Fiona R. Kolbinger

Eung-Joo Lee

Paolo Fiorini

Daochang Liu

Franziska Mathis-Ullrich

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