Fiona R. Kolbinger

Scopus Publications

Artificial intelligence for surgical scene understanding: a systematic review and reporting quality meta-analysis
Matthias Carstens, Shubha Vasisht, Zheyuan Zhang, Iulia Barbur, Annika Reinke, et al.
Npj Digital Medicine, 2026
Surgical scene understanding (SSU) uses artificial intelligence (AI) to interpret visual data from surgeries, such as laparoscopic videos. Despite promising foundational research on instrument and anatomy recognition, clinical adoption remains minimal. This systematic review and meta-analysis (PROSPERO: CRD420251005301) evaluates current SSU research in minimally invasive abdominal surgery, focusing on data curation, model design, validation, reporting standards, and clinical relevance. A total of 188 studies were reviewed. Most relied on small, single-center datasets (70.7%), primarily laparoscopic cholecystectomies (59.0%), reflecting an overall narrow topical breadth. Validation practices were often weak, rarely involving external datasets (10.1%) or clinical experts. Few studies addressed clinical translation (5.9%), model performance variability estimation (38.3%), or made code available (29.8%). Overall, limited progress toward clinical integration has been made over the past decade. Our findings highlight the need for diverse, multi-institutional datasets, robust validation practices, and clinically driven development to unlock the full potential of SSU in surgical practice.
Artificial Intelligence-Based Analysis of Laparoscopic Imaging for Intraoperative Surgical Decision Support
Zheyuan Zhang, Pietro Mascagni, Annika Reinke, Pieter De Backer, Wouter Bogaert, et al.
Annual Review of Biomedical Engineering, 2026
Across surgical specialties, minimally invasive (laparoscopic) surgery has become a standard technique, as it is associated with less trauma, reduced postoperative complication rates, and quicker recovery for patients as compared with open surgery. Due to the limited field of view and limited haptic feedback, the surgical decision-making process in laparoscopic surgery is currently guided solely by surgeons’ visual interpretation of the laparoscopic video stream. Modern artificial intelligence (AI) methods excel at the interpretation of visual data and find applications in clinical routine in fields such as radiology and endoscopy. AI methods could help augment laparoscopic surgery through objective real-time analysis of the laparoscopic video stream. Research studies have demonstrated the feasibility of AI-based surgical scene and process understanding. This review provides an overview of these AI applications, focusing on approaches that could, in the next decade, be translated into intraoperative surgical decision support tools for increased surgical quality and patient safety.
A Decade of C3 Glomerulopathy—A Nationwide Cohort Study
Rick H. Overwijk, Fiona R. Kolbinger, Mark Eijgelsheim, Olaf M. Dekkers, Andreas Kronbichler, et al.
Kidney International Reports, 2026
Federated EndoViT: Pretraining Vision Transformers via Federated Learning on Endoscopic Image Collections
Proceedings of Machine Learning Research, 2026
Mission Balance: Generating Under-Represented Class Samples Using Video Diffusion Models
Danush Kumar Venkatesh, Isabel Funke, Micha Pfeiffer, Fiona Kolbinger, Hanna Maria Schmeiser, et al.
Lecture Notes in Computer Science, 2026
Histopathological evaluation of abdominal aortic aneurysms with deep learning
Fiona R. Kolbinger, Omar S. M. El Nahhas, Maja Carina Nackenhorst, Christine Brostjan, Wolf Eilenberg, et al.
Diagnostic Pathology, 2025
Adaptive validation strategies for real-world clinical artificial intelligence
Fiona R. Kolbinger, Jakob Nikolas Kather
Nature Computational Science, 2025
Translational challenges and clinical potential of artificial intelligence in minimally invasive surgery
Matthias Carstens, Micha Pfeiffer, Stefanie Speidel, Marius Distler, Jürgen Weitz, et al.
Chirurgie Germany, 2025
Zusammenfassung Künstliche Intelligenz (KI) bietet enormes Potenzial für die Chirurgie. Anwendungsfelder reichen von interdisziplinärer Therapiestratifizierung über die Unterstützung der Operationsplanung bis zur Entscheidungsunterstützung im Operationssaal, die im Fokus dieses Beitrags steht. Künstliche neuronale Netzwerke zur Analyse chirurgischer Videos können chirurgische Sicherheit, Effizienz und Planbarkeit verbessern. Voraussetzung dafür sind hochwertige, vielfältige (Meta‑)Daten, deren Annotation, Training und Validierung komplexe Anforderungen stellen. Trotz technischer Fortschritte scheitert die klinische Umsetzung bis dato oft an fehlender Datenstandardisierung, unzureichender Infrastruktur, regulatorischen Hürden und ethischen Unsicherheiten. Viele Modelle bleiben Black Boxes, was Akzeptanz und Vertrauen hemmt. Systeme müssen zudem robust, transparent und praktikabel in klinische Abläufe integrierbar sein. Um die klinische Translation von KI in der Chirurgie zu fördern, sind konsequente Datenerhebungsstrategien, datenschutzkonforme Lernverfahren, Explainable AI und Human-in-the-loop -Ansätze entscheidend. Auch regulatorische Rahmenbedingungen wie die EU Medical Device Regulation bzw. das Medizinprodukterecht-Durchführungsgesetz und der EU AI Act müssen KI-spezifisch für den medizinischen und insbesondere den interventionellen Bereich weiterentwickelt werden, um sichere, interdisziplinäre Assistenztechnologien im Operationssaal zu ermöglichen, die den chirurgischen Alltag sinnvoll ergänzen.
Postoperative complication management: How do large language models measure up to human expertise?
Sophie-Caroline Schwarzkopf, Jean-Paul Bereuter, Mark Enrik Geissler, Jürgen Weitz, Marius Distler, et al.
Plos Digital Health, 2025
Managing postoperative complications is an essential part of surgical care and largely depends on the medical team’s experience. Large Language Models (LLMs) have demonstrated immense potential in supporting medical professionals. To evaluate the potential of LLMs in surgical patient care, we compared the performance of three state-of-the-art LLMs in managing postoperative complications to that of a panel of medical professionals based on six postsurgical patient cases. Six realistic postoperative patient cases were queried using GPT-3, GPT-4, and Gemini-Advanced and presented to human surgical caregivers. Humans and LLMs provided a triage assessment, an initial suspected diagnosis, and an acute management plan, including initial diagnostic and therapeutic measures. Responses were compared based on medical contextual correctness, coherence, and completeness. In comparison to human caregivers, GPT-3 and GPT-4 possess considerable competencies in correctly identifying postoperative complications (humans: 76.3% vs. GPT-3: 75.0% vs. GPT-4: 96.7%, p = 0.47) as well as triaging patients accordingly (humans: 84.8% vs. GPT-3: 50% vs. GPT-4: 38.3%, p = 0.19). With regard to diagnostic and therapeutic management of postoperative complications, GPT-3 and GPT-4 provided comprehensive management plans. Gemini-Advanced often provided no diagnostic or therapeutic recommendations and censored its outputs. In summary, LLMs can accurately interpret postoperative care scenarios and provide comprehensive management recommendations. These results showcase the improvements in LLMs performance with regard to postoperative surgical use cases and provide evidence for their potential value to support and augment surgical routine care.
Artificial intelligence in pancreatic intraductal papillary mucinous neoplasm imaging: A systematic review
Muhammad Ibtsaam Qadir, Jackson A. Baril, Michele T. Yip-Schneider, Duane Schonlau, Thi Thanh Thoa Tran, et al.
Plos Digital Health, 2025
Based on the Fukuoka and Kyoto international consensus guidelines, the current clinical management of intraductal papillary mucinous neoplasm (IPMN) largely depends on imaging features. While these criteria are highly sensitive in detecting high-risk IPMN, they lack specificity, resulting in surgical overtreatment. Artificial Intelligence (AI)-based medical image analysis has the potential to augment the clinical management of IPMNs by improving diagnostic accuracy. Based on a systematic review of the academic literature on AI in IPMN imaging, 1041 publications were identified of which 25 published studies were included in the analysis. The studies were stratified based on prediction target, underlying data type and imaging modality, patient cohort size, and stage of clinical translation and were subsequently analyzed to identify trends and gaps in the field. Research on AI in IPMN imaging has been increasing in recent years. The majority of studies utilized CT imaging to train computational models. Most studies presented computational models developed on single-center datasets (n = 11,44%) and included less than 250 patients (n = 18,72%). Methodologically, convolutional neural network (CNN)-based algorithms were most commonly used. Thematically, most studies reported models augmenting differential diagnosis (n = 9,36%) or risk stratification (n = 10,40%) rather than IPMN detection (n = 5,20%) or IPMN segmentation (n = 2,8%). This systematic review provides a comprehensive overview of the research landscape of AI in IPMN imaging. Computational models have potential to enhance the accurate and precise stratification of patients with IPMN. Multicenter collaboration and datasets comprising various modalities are necessary to fully utilize this potential, alongside concerted efforts towards clinical translation.
Pancreatoduodenectomy versus total pancreatectomy and simultaneous intraportal islet autotransplantation for periampullary cancer at high-risk of postoperative pancreatic fistula (XANDTX-trial): Protocol of a randomized controlled pilot trial
Sebastian Hempel, Fiona R. Kolbinger, Florian Oehme, Olga Radulova-Mauersberger, Janine Schmid, et al.
Plos One, 2025
AutoFRS: an externally validated, annotation-free approach to computational preoperative complication risk stratification in pancreatic surgery - an experimental study
Fiona R. Kolbinger, Nithya Bhasker, Felix Schön, Daniel Cser, Alex Zwanenburg, et al.
International Journal of Surgery London England, 2025
Benchmarking Vision Capabilities of Large Language Models in Surgical Examination Questions
Jean-Paul Bereuter, Mark Enrik Geissler, Anna Klimova, Robert-Patrick Steiner, Kevin Pfeiffer, et al.
Journal of Surgical Education, 2025
From marginal gains to clinical utility: machine learning–based percutaneous coronary intervention risk prediction models
Muhammad Ibtsaam Qadir, Ravi S Hira, Fiona R Kolbinger
European Heart Journal Digital Health, 2025
Data Augmentation for Surgical Scene Segmentation with Anatomy-Aware Diffusion Models
Danush Kumar Venkatesh, Dominik Rivoir, Micha Pfeiffer, Fiona Kolbinger, Stefanie Speidel
Proceedings 2025 IEEE Winter Conference on Applications of Computer Vision Wacv 2025, 2025
The Performance of Artificial Intelligence on a National Medical Licensing Examination
Mark Enrik Geissler, Merle Goeben, Kira A. Glasmacher, Jean-Paul Bereuter, Rona Berit Geissler, et al.
Deutsches Arzteblatt International, 2024
Reporting guidelines in medical artificial intelligence: a systematic review and meta-analysis
Fiona R. Kolbinger, Gregory P. Veldhuizen, Jiefu Zhu, Daniel Truhn, Jakob Nikolas Kather
Communications Medicine, 2024
Artificial Intelligence for context-aware surgical guidance in complex robot-assisted oncological procedures: An exploratory feasibility study
Fiona R. Kolbinger, Sebastian Bodenstedt, Matthias Carstens, Stefan Leger, Stefanie Krell, et al.
European Journal of Surgical Oncology, 2024
Author Correction: Regression-based Deep-Learning predicts molecular biomarkers from pathology slides (Nature Communications, (2024), 15, 1, (1253), 10.1038/s41467-024-45589-1)
Omar S. M. El Nahhas, Chiara M. L. Loeffler, Zunamys I. Carrero, Marko van Treeck, Fiona R. Kolbinger, et al.
Nature Communications, 2024
Regression-based Deep-Learning predicts molecular biomarkers from pathology slides
Omar S. M. El Nahhas, Chiara M. L. Loeffler, Zunamys I. Carrero, Marko van Treeck, Fiona R. Kolbinger, et al.
Nature Communications, 2024
One model to use them all: training a segmentation model with complementary datasets
Alexander C. Jenke, Sebastian Bodenstedt, Fiona R. Kolbinger, Marius Distler, Jürgen Weitz, et al.
International Journal of Computer Assisted Radiology and Surgery, 2024
Exploring semantic consistency in unpaired image translation to generate data for surgical applications
Danush Kumar Venkatesh, Dominik Rivoir, Micha Pfeiffer, Fiona Kolbinger, Marius Distler, et al.
International Journal of Computer Assisted Radiology and Surgery, 2024
Portable droplet-based real-time monitoring of pancreatic α-amylase in postoperative patients
Xinne Zhao, Fiona R. Kolbinger, Marius Distler, Jürgen Weitz, Denys Makarov, et al.
Biosensors and Bioelectronics, 2024
Strategies to Improve Real-World Applicability of Laparoscopic Anatomy Segmentation Models
Fiona R. Kolbinger, Jiangpeng He, Jinge Ma, Fengqing Zhu
IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, 2024
SeeSaw: Learning Soft Tissue Deformation from Laparoscopy Videos with GNNs
Reuben Docea, Jinjing Xu, Wei Ling, Alexander C. Jenke, Fiona R. Kolbinger, et al.
IEEE Transactions on Biomedical Engineering, 2024
Importance of the Data in the Surgical Environment
Dominik Rivoir, Martin Wagner, Sebastian Bodenstedt, Keno März, Fiona Kolbinger, et al.
Artificial Intelligence and the Perspective of Autonomous Surgery, 2024
Exercise-Induced Fluid Retention, Cardiac Volume Overload, and Peripheral Edema in Ultra-Distance Cyclists
Philipp Gauckler, Jana S. Kesenheimer, Johannes Leierer, Maren Kruus, Michael Schreinlechner, et al.
Kidney International Reports, 2024
The Dresden Surgical Anatomy Dataset for Abdominal Organ Segmentation in Surgical Data Science
Matthias Carstens, Franziska M. Rinner, Sebastian Bodenstedt, Alexander C. Jenke, Jürgen Weitz, et al.
Scientific Data, 2023
Significance of Distinct Liquid Biopsy Compartments in Evaluating Somatic Mutations for Targeted Therapy Selection in Cancer of Unknown Primary
Fiona R. Kolbinger, Vincent Bernard, Jaewon J. Lee, Bret M. Stephens, Vittorio Branchi, et al.
Journal of Gastrointestinal Cancer, 2023
Prediction of clinically relevant postoperative pancreatic fistula using radiomic features and preoperative data
Nithya Bhasker, Fiona R. Kolbinger, Nadiia Skorobohach, Alex Zwanenburg, Steffen Löck, et al.
Scientific Reports, 2023
The future landscape of large language models in medicine
Jan Clusmann, Fiona R. Kolbinger, Hannah Sophie Muti, Zunamys I. Carrero, Jan-Niklas Eckardt, et al.
Communications Medicine, 2023
Active learning for extracting surgomic features in robot-assisted minimally invasive esophagectomy: a prospective annotation study
Johanna M. Brandenburg, Alexander C. Jenke, Antonia Stern, Marie T. J. Daum, André Schulze, et al.
Surgical Endoscopy, 2023
Anatomy segmentation in laparoscopic surgery: comparison of machine learning and human expertise - an experimental study
Fiona R. Kolbinger, Franziska M. Rinner, Alexander C. Jenke, Matthias Carstens, Stefanie Krell, et al.
International Journal of Surgery London England, 2023
Direct prediction of genetic aberrations from pathology images in gastric cancer with swarm learning
Oliver Lester Saldanha, Hannah Sophie Muti, Heike I. Grabsch, Rupert Langer, Bastian Dislich, et al.
Gastric Cancer, 2023
Why do people cycle (a lot)? A multivariate approach on mental health, personality traits and motivation as determinants for cycling ambition
Jana S. Kesenheimer, Christina Sagioglou, Andreas Kronbichler, Philipp Gauckler, Fiona R. Kolbinger
Journal of Applied Sport Psychology, 2023
Case-Based Serious Gaming for Complication Management in Colorectal and Pancreatic Surgery: Prospective Observational Study
Sophie-Caroline Schwarzkopf, Marius Distler, Thilo Welsch, Grit Krause-Jüttler, Jürgen Weitz, et al.
Jmir Serious Games, 2023
The image-based preoperative fistula risk score (preFRS) predicts postoperative pancreatic fistula in patients undergoing pancreatic head resection
Fiona R. Kolbinger, Julia Lambrecht, Stefan Leger, Till Ittermann, Stefanie Speidel, et al.
Scientific Reports, 2022
Surgomics: personalized prediction of morbidity, mortality and long-term outcome in surgery using machine learning on multimodal data
Martin Wagner, Johanna M. Brandenburg, Sebastian Bodenstedt, André Schulze, Alexander C. Jenke, et al.
Surgical Endoscopy, 2022
The use and future perspective of Artificial Intelligence—A survey among German surgeons
Mathieu Pecqueux, Carina Riediger, Marius Distler, Florian Oehme, Ulrich Bork, et al.
Frontiers in Public Health, 2022
Optimal treatment sequence in colorectal cancer patients with synchronous liver metastases
Fiona R. Kolbinger, Johanna Kirchberg, Jürgen Weitz, Johannes Fritzmann
Coloproctology, 2022
Artificial Intelligence in Colorectal Cancer Surgery: Present and Future Perspectives
Giuseppe Quero, Pietro Mascagni, Fiona R. Kolbinger, Claudio Fiorillo, Davide De Sio, et al.
Cancers, 2022
The Impact of Pancreatic Head Resection on Blood Glucose Homeostasis in Patients with Chronic Pancreatitis
Sebastian Hempel, Florian Oehme, Florian Ehehalt, Michele Solimena, Fiona Kolbinger, et al.
Journal of Clinical Medicine, 2022
A Laparoscopic Liver Navigation Pipeline with Minimal Setup Requirements
Reuben Docea, Micha Pfeiffer, Jan Muller, Katja Krug, Matthias Hardner, et al.
Biocas 2022 IEEE Biomedical Circuits and Systems Conference Intelligent Biomedical Systems for A Better Future Proceedings, 2022
Edema-like symptoms are common in ultra-distance cyclists and driven by overdrinking, use of analgesics and female sex – a study of 919 athletes
Philipp Gauckler, Jana S. Kesenheimer, Andreas Kronbichler, Fiona R. Kolbinger
Journal of the International Society of Sports Nutrition, 2021
More is More? Total Pancreatectomy for Periampullary Cancer as an Alternative in Patients with High-Risk Pancreatic Anastomosis: A Propensity Score-Matched Analysis
Sebastian Hempel, Florian Oehme, Ermal Tahirukaj, Fiona R. Kolbinger, Benjamin Müssle, et al.
Annals of Surgical Oncology, 2021
Long-Term Temporally Consistent Unpaired Video Translation from Simulated Surgical 3D Data
Dominik Rivoir, Micha Pfeiffer, Reuben Docea, Fiona Kolbinger, Carina Riediger, et al.
Proceedings of the IEEE International Conference on Computer Vision, 2021
Simultaneous localisation and mapping for laparoscopic liver navigation: A comparative evaluation study
Reuben Docea, Micha Pfeiffer, Sebastian Bodenstedt, Fiona R. Kolbinger, Lukas Höller, et al.
Progress in Biomedical Optics and Imaging Proceedings of SPIE, 2021
Dual role of HDAC10 in lysosomal exocytosis and DNA repair promotes neuroblastoma chemoresistance
Johannes Ridinger, Emily Koeneke, Fiona R. Kolbinger, Katharina Koerholz, Siavosh Mahboobi, et al.
Scientific Reports, 2018
The HDAC6/8/10 inhibitor TH34 induces DNA damage-mediated cell death in human high-grade neuroblastoma cell lines
Fiona R. Kolbinger, Emily Koeneke, Johannes Ridinger, Tino Heimburg, Michael Müller, et al.
Archives of Toxicology, 2018
A kinome-wide RNAi screen identifies ALK as a target to sensitize neuroblastoma cells for HDAC8-inhibitor treatment
Jing Shen, Sara Najafi, Sina Stäble, Johannes Fabian, Emily Koeneke, et al.
Cell Death and Differentiation, 2018
Structure-Based Design and Biological Characterization of Selective Histone Deacetylase 8 (HDAC8) Inhibitors with Anti-Neuroblastoma Activity
Tino Heimburg, Fiona R. Kolbinger, Patrik Zeyen, Ehab Ghazy, Daniel Herp, et al.
Journal of Medicinal Chemistry, 2017
Targeting histone deacetylase 8 as a therapeutic approach to cancer and neurodegenerative diseases
Alokta Chakrabarti, Jelena Melesina, Fiona R Kolbinger, Ina Oehme, Johanna Senger, et al.
Future Medicinal Chemistry, 2016

RECENT SCHOLAR PUBLICATIONS

Artificial Intelligence–Based Analysis of Laparoscopic Imaging for Intraoperative Surgical Decision Support
Z Zhang, P Mascagni, A Reinke, P De Backer, W Bogaert, M Mezzina, ...
Annual Review of Biomedical Engineering 28 (1), 135-162 , 2026
2026
Citations: 1
Adaptive-CaRe: Adaptive Causal Regularization for Robust Outcome Prediction
N Bhasker, FR Kolbinger, S Hu, G Kutyniok, S Speidel
arXiv preprint arXiv:2602.06611 , 2026
2026
Field strength-dependent performance variability in deep learning-based analysis of magnetic resonance imaging
MI Qadir, D Schonlau, U Dydak, FR Kolbinger
arXiv preprint arXiv:2512.22176 , 2025
2025
Artificial intelligence for surgical scene understanding: a systematic review and reporting quality meta-analysis
M Carstens, S Vasisht, Z Zhang, I Barbur, A Reinke, L Maier-Hein, ...
npj Digital Medicine , 2025
2025
Citations: 9
6 Fingers, 1 Kidney: Natural Adversarial Medical Images Reveal Critical Weaknesses of Vision-Language Models
L Mayer, P Kalinowski, C Ebersbach, M Knopp, T Rädsch, ...
arXiv preprint arXiv:2512.04238 , 2025
2025
Citations: 2
Adaptive validation strategies for real-world clinical artificial intelligence
FR Kolbinger, JN Kather
Nature Computational Science, 1-7 , 2025
2025
Citations: 5
A Decade of C3 glomerulopathy-a nationwide cohort study
RH Overwijk, F Kolbinger, M Eijgelsheim, O Dekkers, A Kronbichler, ...
Kidney International Reports , 2025
2025
Current validation practice undermines surgical AI development
A Reinke, ZO Li, MD Tizabi, P André, M Knopp, MM Rother, IP Machado, ...
arXiv preprint arXiv:2511.03769 , 2025
2025
Citations: 3
Translationale Herausforderungen und klinisches Potenzial von künstlicher Intelligenz in der minimal-invasiven Chirurgie
M Carstens, M Pfeiffer, S Speidel, M Distler, J Weitz, FR Kolbinger
Die Chirurgie 96 (11), 901-906 , 2025
2025
Citations: 1
Vision-language models for automated video analysis and documentation in laparoscopic surgery: a proof-of-concept study
EH Stueker, FR Kolbinger, OL Saldanha, D Digomann, S Pistorius, ...
International Journal of Surgery 111 (11), 7777-7786 , 2025
2025
Citations: 9
Federated Learning for Surgical Vision in Appendicitis Classification: Results of the FedSurg EndoVis 2024 Challenge
M Kirchner, H Hoffmann, AC Jenke, OL Saldanha, K Pfeiffer, W Kanjo, ...
arXiv preprint arXiv:2510.04772 , 2025
2025
# 2442 Ten years of C3 glomerulopathy: a nationwide cohort study
R Overwijk, F Kolbinger, M Eijgelsheim, O Dekkers, A Kronbichler, ...
Nephrology Dialysis Transplantation 40 (Supplement_3), gfaf116. 1281 , 2025
2025
Decentralized, privacy-preserving surgical video analysis with Swarm Learning
OL Saldanha, K Pfeiffer, S Bodenstedt, M Kirchner, AC Jenke, C Barata, ...
medRxiv, 2025.10. 02.25337106 , 2025
2025
Citations: 1
Mission balance: Generating under-represented class samples using video diffusion models
DK Venkatesh, I Funke, M Pfeiffer, F Kolbinger, HM Schmeiser, M Distler, ...
International Conference on Medical Image Computing and Computer-Assisted … , 2025
2025
Citations: 4
Histopathological evaluation of abdominal aortic aneurysms with deep learning
FR Kolbinger, OSM El Nahhas, MC Nackenhorst, C Brostjan, W Eilenberg, ...
Diagnostic Pathology 20 (1), 104 , 2025
2025
Citations: 2
Appendix300: A multi-institutional laparoscopic appendectomy video dataset for computational modeling tasks
FR Kolbinger, M Kirchner, K Pfeiffer, S Bodenstedt, AC Jenke, J Barthel, ...
medRxiv, 2025.09. 05.25335174 , 2025
2025
Citations: 2
Translational challenges and clinical potential of artificial intelligence in minimally invasive surgery
M Carstens, M Pfeiffer, S Speidel, M Distler, J Weitz, FR Kolbinger
Chirurgie (Heidelberg, Germany) , 2025
2025
Postoperative complication management: How do large language models measure up to human expertise?
SC Schwarzkopf, JP Bereuter, ME Geissler, J Weitz, M Distler, ...
PLOS Digital Health 4 (8), e0000933 , 2025
2025
Pancreatoduodenectomy versus total pancreatectomy and simultaneous intraportal islet autotransplantation for periampullary cancer at high-risk of postoperative pancreatic …
S Hempel, FR Kolbinger, F Oehme, O Radulova-Mauersberger, J Schmid, ...
Plos one 20 (7), e0327949 , 2025
2025
PIVOTS: Aligning unseen Structures using Preoperative to Intraoperative Volume-To-Surface Registration for Liver Navigation
P Liu, B Güttner, Y Su, C Li, J Xu, M Liu, Z Min, A Zhylka, J Smit, K Olthof, ...
arXiv preprint arXiv:2507.20337 , 2025
2025

MOST CITED SCHOLAR PUBLICATIONS

The future landscape of large language models in medicine
J Clusmann, FR Kolbinger, HS Muti, ZI Carrero, JN Eckardt, NG Laleh, ...
Communications medicine 3 (1), 141 , 2023
2023
Citations: 1272
Reporting guidelines in medical artificial intelligence: a systematic review and meta-analysis
FR Kolbinger, GP Veldhuizen, J Zhu, D Truhn, JN Kather
Communications Medicine 4 (1), 71 , 2024
2024
Citations: 143
Regression-based Deep-Learning predicts molecular biomarkers from pathology slides
OSM El Nahhas, CML Loeffler, ZI Carrero, M van Treeck, FR Kolbinger, ...
nature communications 15 (1), 1253 , 2024
2024
Citations: 132
Targeting histone deacetylase 8 as a therapeutic approach to cancer and neurodegenerative diseases
A Chakrabarti, J Melesina, FR Kolbinger, I Oehme, J Senger, O Witt, ...
Future medicinal chemistry 8 (13), 1609-1634 , 2016
2016
Citations: 115
The Dresden surgical anatomy dataset for abdominal organ segmentation in surgical data science
M Carstens, FM Rinner, S Bodenstedt, AC Jenke, J Weitz, M Distler, ...
Scientific Data 10 (1), 1-8 , 2023
2023
Citations: 109
Structure-based design and biological characterization of selective histone deacetylase 8 (HDAC8) inhibitors with anti-neuroblastoma activity
T Heimburg, FR Kolbinger, P Zeyen, E Ghazy, D Herp, K Schmidtkunz, ...
Journal of medicinal chemistry 60 (24), 10188-10204 , 2017
2017
Citations: 103
Dual role of HDAC10 in lysosomal exocytosis and DNA repair promotes neuroblastoma chemoresistance
J Ridinger, E Koeneke, FR Kolbinger, K Koerholz, S Mahboobi, L Hellweg, ...
Scientific reports 8 (1), 10039 , 2018
2018
Citations: 65
Artificial intelligence in colorectal cancer surgery: present and future perspectives
G Quero, P Mascagni, FR Kolbinger, C Fiorillo, D De Sio, F Longo, ...
Cancers 14 (15), 3803 , 2022
2022
Citations: 61
Anatomy segmentation in laparoscopic surgery: comparison of machine learning and human expertise–an experimental study
FR Kolbinger, FM Rinner, AC Jenke, M Carstens, S Krell, S Leger, ...
International Journal of Surgery 109 (10), 2962-2974 , 2023
2023
Citations: 58
Artificial Intelligence for context-aware surgical guidance in complex robot-assisted oncological procedures: An exploratory feasibility study
FR Kolbinger, S Bodenstedt, M Carstens, S Leger, S Krell, FM Rinner, ...
European Journal of Surgical Oncology, 106996 , 2023
2023
Citations: 57
The HDAC6/8/10 inhibitor TH34 induces DNA damage-mediated cell death in human high-grade neuroblastoma cell lines
FR Kolbinger, E Koeneke, J Ridinger, T Heimburg, M Müller, T Bayer, ...
Archives of toxicology 92 (8), 2649-2664 , 2018
2018
Citations: 51
Long-term temporally consistent unpaired video translation from simulated surgical 3d data
D Rivoir, M Pfeiffer, R Docea, F Kolbinger, C Riediger, J Weitz, S Speidel
Proceedings of the IEEE/CVF international conference on computer vision … , 2021
2021
Citations: 49
The use and future perspective of Artificial Intelligence—A survey among German surgeons
M Pecqueux, C Riediger, M Distler, F Oehme, U Bork, FR Kolbinger, ...
Frontiers in public health 10, 982335 , 2022
2022
Citations: 47
A kinome-wide RNAi screen identifies ALK as a target to sensitize neuroblastoma cells for HDAC8-inhibitor treatment
J Shen, S Najafi, S Stäble, J Fabian, E Koeneke, FR Kolbinger, JK Wrobel, ...
Cell Death & Differentiation 25 (12), 2053-2070 , 2018
2018
Citations: 43
Direct prediction of genetic aberrations from pathology images in gastric cancer with swarm learning
OL Saldanha, HS Muti, HI Grabsch, R Langer, B Dislich, M Kohlruss, ...
Gastric cancer 26 (2), 264-274 , 2023
2023
Citations: 36
Surgomics: personalized prediction of morbidity, mortality and long-term outcome in surgery using machine learning on multimodal data
M Wagner, JM Brandenburg, S Bodenstedt, A Schulze, AC Jenke, A Stern, ...
Surgical endoscopy 36 (11), 8568-8591 , 2022
2022
Citations: 35
The importance of machine learning in autonomous actions for surgical decision making
M Wagner, S Bodenstedt, M Daum, A Schulze, R Younis, J Brandenburg, ...
Artificial Intelligence Surgery 2 (2), 64-79 , 2022
2022
Citations: 30
More is more? Total pancreatectomy for periampullary cancer as an alternative in patients with high-risk pancreatic anastomosis: a propensity score-matched analysis
S Hempel, F Oehme, E Tahirukaj, FR Kolbinger, B Müssle, T Welsch, ...
Annals of Surgical Oncology 28 (13), 8309-8317 , 2021
2021
Citations: 24
Why do people cycle (a lot)? A multivariate approach on mental health, personality traits and motivation as determinants for cycling ambition
JS Kesenheimer, C Sagioglou, A Kronbichler, P Gauckler, FR Kolbinger
Journal of Applied Sport Psychology 35 (6), 1005-1025 , 2023
2023
Citations: 19
Active learning for extracting surgomic features in robot-assisted minimally invasive esophagectomy: a prospective annotation study
JM Brandenburg, AC Jenke, A Stern, MTJ Daum, A Schulze, R Younis, ...
Surgical endoscopy 37 (11), 8577-8593 , 2023
2023
Citations: 19

Fiona R. Kolbinger

RESEARCH, TEACHING, or OTHER INTERESTS

Scopus Publications

RECENT SCHOLAR PUBLICATIONS

MOST CITED SCHOLAR PUBLICATIONS