Status · validation
GoNoGoNet
AI-guided Go/No-Go surgical safety zone detection
Computer vision for surgical dissection safety-zone recognition and intraoperative decision support research.
The Problem
What is broken clinically?
During laparoscopic cholecystectomy, safe progress depends on correctly interpreting operative anatomy, dissection planes, and regions where instrument entry may increase risk. Computer vision can help study those visual cues, but any system must remain bounded as research and validation support rather than autonomous guidance.
Clinical Need
Why this matters in surgery
GoNoGoNet focuses on Go/No-Go surgical safety zone recognition for education, review, and future decision-support research. The clinical need is a cautious, interpretable way to study safety-zone visualization in laparoscopic surgery while preserving surgeon judgment as the final authority.
Data Sources
Where the data comes from
The project is linked to published PubMed records involving laparoscopic cholecystectomy video-frame workflows and expert-informed validation. Local dataset details, governance, and prospective validation cohorts should be confirmed before describing deployment or clinical use.
Methods
Technical approach
The system is framed as surgical computer vision using segmentation and safety-zone recognition methods. Outputs may be shown as mask, overlay, or heatmap-style previews that help reviewers understand possible Go and No-Go regions in operative video.
Validation Plan
How the claims will be tested
Validation should remain staged: retrospective comparison with expert annotations, external dataset testing, human-factors review, prospective feasibility work, and assessment of generalizability across surgeons, institutions, anatomy, imaging conditions, and case complexity.
Current Status
What stage we're at
GoNoGoNet is listed as a validation-stage research project. It is not a replacement for surgeon judgment and is not described as autonomous clinical decision-making software.
Model Card
Intended use, readiness, and limitations
A-STAR model card
GoNoGoNet
AI-guided Go/No-Go surgical safety zone detection
- Intended use
- Research decision-support for identifying laparoscopic dissection regions that may represent Go and No-Go safety zones. Advisory only; it is not a replacement for surgeon judgment.
- Clinical phase
- Intraoperative surgical guidance and education
- Input data
- Laparoscopic or operative video frames
- Procedure context for cholecystectomy-oriented validation workflows
- Output
- Safety-zone / Go-No-Go visual guidance
- Pixel-level or heatmap-style model output for review
- Model/pipeline
- Surgical computer vision and semantic segmentation workflow for safety-zone recognition in laparoscopic cholecystectomy contexts.
- Validation status
- Published retrospective and experimental validation records are linked; broader translational validation and generalizability testing remain required.
- Deployment readiness
- Research and validation phase. Not designed for autonomous clinical decision-making.
- Limitations
- Requires careful validation across datasets, institutions, surgeons, anatomy, case complexity, and real-time operating room constraints.
Project media
Demos and output previews
GoNoGoNet uses the local optimized preview when available. Future clipped demo files can be placed at public/projects/media/gonogonet-demo.mp4, public/projects/media/gonogonet-demo.gif, or public/projects/media/gonogonet-demo.avif.
Team
People behind this project
Collaborating Institutions
Partners on this project
Related Publications
Research outputs
Laplante SJ, Namazi B, Kiani P, Hashimoto DA, Alseidi A, Madani A
Surgical Endoscopy · 2023
Metadata to confirm
Surgical Endoscopy · 2023
Related talks
Courses, webinars, and presentations
Computer Vision Assisted Surgery: Current Landscape and Future Directions
Simon J. Laplante, M.D.
ASMBS Minnesota State Chapter Meeting / Minneapolis, Minnesota
Dr. Simon J. Laplante presented on the current landscape and future directions of computer vision-assisted surgery.
Computer Vision Assisted Surgery: Current Landscape and Future Directions
Simon J. Laplante, M.D.
Sixth IBC Oxford University World Congress 2025 / University of Oxford, United Kingdom
Dr. Laplante presented on the current landscape and future directions of computer vision-assisted surgery.
Pathway to Safer Cholecystectomy: Using the Validated GoNoGoNet to Demonstrate the Potential Clinical and Educational Applications of Surgical Computer Vision
Simon J. Laplante, M.D.
AI Summit Generative & Multimodal AI - Potentials and Challenges / Rochester, Minnesota
Dr. Laplante discussed the use of validated GoNoGoNet to demonstrate potential clinical and educational applications of surgical computer vision in safer cholecystectomy.
Get involved
Interested in collaborating on GoNoGoNet? We welcome clinical partnerships, dataset contributions, and research collaboration.
Reach out