Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Search research articles

Related Experiment Video

Updated: Sep 11, 2025

Simulator Training for Endovascular Neurosurgery

Simulator Training for Endovascular Neurosurgery

Published on: May 6, 2020

Graph Neural Networks for Realistic Bleeding Prediction in Surgical Simulators.

Yasar C Kakdas¹, Suvranu De², Doga Demirel³

¹Department of Computer Science, Florida Polytechnic University, Lakeland, FL, USA.

Journal of Imaging Informatics in Medicine

|August 12, 2025

Summary

This summary is machine-generated.

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

Analysis and objective assessment of transoral robotic surgery.

International journal of computer assisted radiology and surgery·2026

Same author

Real-Time Haptic-Based Soft Body Suturing in Virtual Open Surgery Simulations.

Computers & graphics·2025

Same author

Time and Expertise in Open Structural Rhinoplasty: A Task-Based Analysis Using Hierarchical Task Analysis and Machine Learning.

The Journal of craniofacial surgery·2025

Same author

Validity of a virtual reality-based straight coloanal anastomosis simulator.

International journal of computer assisted radiology and surgery·2024

Same author

Endoscopic sleeve gastroplasty: stomach location and task classification for evaluation using artificial intelligence.

International journal of computer assisted radiology and surgery·2024

Same author

GPU based Position Based Dynamics for Surgical Simulators.

HCI in Games : 5th International Conference, HCI-Games 2023, held as part of the 25th HCI International Conference, HCII 2023, Copenhagen, Denmark, July 23-28, 2023, proceedings. Part I. HCI-Games (Conference) (5th : 2023 : Copenhagen, ...·2023

Same journal

OpenDicomViewer: A Lightweight Open-Source DICOM Viewer for macOS Built with Swift.

Journal of imaging informatics in medicine·2026

Same journal

Multimodal Large Language Model for Zero-Shot L3 Body Composition Segmentation on CT: Improved Accuracy via Automated Candidate Selection.

Journal of imaging informatics in medicine·2026

Same journal

Scalable Left Ventricular ROI Annotation for Stress Perfusion Cardiac MRI using Deep Learning with Visual Refinement.

Journal of imaging informatics in medicine·2026

Same journal

RE-LIG: A Faithfulness-Driven Layer Integrated Gradients Framework for Explainable Medical Visual Question Answering.

Journal of imaging informatics in medicine·2026

Same journal

Bridging Parallel Disciplines: An Integrated Workshop for Clinical and Imaging Informatics Training.

Journal of imaging informatics in medicine·2026

Same journal

CRAF-Net: A Fine-Grained Cross-Channel Attention Network for Preoperative Microvascular Invasion Grading in Hepatocellular Carcinoma via DCE-MRI.

Journal of imaging informatics in medicine·2026

See all related articles

This study uses graph neural networks to predict internal bleeding risk from medical scans, enhancing surgical simulator realism for trauma emergencies. The novel approach accurately identifies hemorrhage probability, aiding rapid diagnosis and lifesaving interventions.

Area of Science:

Medical Imaging Analysis
Artificial Intelligence in Healthcare
Computational Physiology

Background:

Internal bleeding detection is critical in emergency medicine, particularly for trauma cases.
Existing surgical simulators lack realistic hemorrhage modeling, limiting training effectiveness.
Accurate prediction of bleeding risk can significantly improve patient outcomes.

Purpose of the Study:

To develop a novel approach for predicting internal bleeding risk using graph neural networks (GNNs).
To enhance the realism of surgical simulators by integrating a bleeding risk prediction model.
To improve emergency response and surgical training for trauma scenarios.

Main Methods:

Medical images (CT, MRI) were segmented and converted into vascular graph representations.

Keywords:

Bleeding prediction Graph neural network Medical imaging Surgical simulator Trauma training Virtual reality

More Related Videos

A Training and Testing System for Performing Vascular Reconstruction In Vitro

A Training and Testing System for Performing Vascular Reconstruction In Vitro

Published on: October 26, 2019

A New Hybrid Quantitative Evaluation Model for Axillary Junctional Hemorrhage in Swine

A New Hybrid Quantitative Evaluation Model for Axillary Junctional Hemorrhage in Swine

Published on: December 6, 2024

Related Experiment Videos

Last Updated: Sep 11, 2025

Simulator Training for Endovascular Neurosurgery

Simulator Training for Endovascular Neurosurgery

Published on: May 6, 2020

A Training and Testing System for Performing Vascular Reconstruction In Vitro

A Training and Testing System for Performing Vascular Reconstruction In Vitro

Published on: October 26, 2019

A New Hybrid Quantitative Evaluation Model for Axillary Junctional Hemorrhage in Swine

A New Hybrid Quantitative Evaluation Model for Axillary Junctional Hemorrhage in Swine

Published on: December 6, 2024

A physics-based heuristic (Hagen-Poiseuille equation) was used to calculate bleeding probability for vessel nodes.

A graph attention network was trained to predict hemorrhage risk from graph data, validated on 1708 vessel graphs.

Main Results:

The GNN model achieved a mean R-squared of 0.86 (up to 0.9188) in predicting bleeding risk.
Low mean training (0.0069) and validation (0.0074) losses were observed.
The model demonstrated robust performance, especially on well-connected vascular graphs, despite data sparsity.

Conclusions:

Graph neural networks offer a powerful tool for predicting internal bleeding risk from medical imaging.
The developed model can be integrated into virtual reality surgical simulators for realistic trauma training.
This approach has the potential to enhance surgical preparedness and improve patient care in emergency situations.