From Surgical Video to Validated AI in 6 Steps
How Chimaera ImViA enables a connected workflow for surgical data
Surgical videos contain valuable information about instruments, procedural steps, surgical workflows, and clinical events. However, video footage alone does not provide reliable data for research, quality management, or artificial intelligence development.
A process chain is required in which all steps are seamlessly connected, from organizing video data and structured annotation to quality control, data management, AI training, and validation.
This is where Chimaera ImViA comes in. The browser-based platform centralizes these steps in a central environment, creating a continuous workflow for surgical video data – without isolated tools and unnecessary breaks between processes.
1. Structure surgical video data centrally
Every AI workflow begins with a reliable data foundation. However, surgical videos are complex and often extensive. They contain information that needs to be systematically captured and structured before it can be used effectively.
With Chimaera ImViA, videos can be uploaded, organized, and enriched with structured metadata in one central location. Project managers can define annotation tasks, label sets, and quality stages. Anonymization and pseudonymization functions support the secure handling of sensitive medical data. These features create a reliable foundation for subsequent workflow steps.
2. Annotate surgical videos efficiently
Annotations allow you to capture what is happening in a surgical video.
For example, they can mark temporal phases, instruments in use, surgical events, or specific procedural steps.
With Chimaera ImViA, teams can annotate videos directly in a browser.
The platform supports temporal segmentation, instrument annotation, and surgical workflow recognition. Other medical data types, including 2D images, 3D volumes, and endoscopic data, can be processed in the same environment.
AI-assisted functions further reduce manual effort. Video Object Segmentation (VOS) allows object masks created on selected keyframes to be propagated across longer video sequences. This saves time and ensures consistent annotations, especially for extensive videos or repetitive tasks.
The goal is not to produce the largest number of annotations possible, but rather to create accurate, usable data for further analysis and AI development.
3. Ensure quality throughout the workflow
High-quality AI models require high-quality training data. Therefore, quality control should not only take place at the end of a project. Rather, it must be an integral part of the workflow from the beginning.
ImViA integrates defined quality steps directly into the annotation workflow. Medical experts can review annotations and Quality Officers can subsequently approve them. Clearly defined roles, responsibilities, and workflow stages establish accountability and support end-to-end traceability.
Project dashboards provide transparency regarding progress, resource usage, and quality status. Thus, annotation becomes more than an isolated task; it becomes a controlled, collaborative process.
4. Connect data, versions and processes
As datasets grow, so does the effort required to manage them. It is particularly important in research projects, clinical studies, and multicenter collaborations to keep track of different versions, metadata, processing steps, and contributors.
Chimaera ImViA combines structured data management with annotation workflows. Images and videos can be managed alongside metadata, documentation, and version information. Processing steps remain transparent, and data pipelines are traceable.
Role-based access controls and integrated project management provide the necessary structure for collaboration across locations and disciplines.
These features keep data and processes connected as projects, teams, and datasets grow.
5. Develop AI models from data
After data is annotated and quality-assured, it can be used to develop and evaluate AI models.
The integrated "AI Model Zoo" is a central environment for managing and using different models. This includes Chimaera models, customer-specific developments, and models from the open-source ecosystem.
Teams can train, test, and apply models without switching platforms. Automated workers and batch processing support scalable data preparation and inference.
Chimaera ImViA can also be connected to established technologies for medical AI workflows through integration with MONAI Application Packages and compatibility with NVIDIA Holoscan. Depending on the application, models can be deployed on desktop GPUs, NVIDIA Jetson, NVIDIA IGX, or enterprise edge AI systems.
6. Validate and further develop results
AI development is not a linear process. Models must be tested, results reviewed, and datasets or annotation guidelines adjusted where necessary.
Chimaera ImViA brings video data, annotations, quality processes, and AI models together in one environment, allowing teams to carry out these iterations efficiently. The platform supports prototyping and validation, and it can be connected to existing development environments and data pipelines via a documented API and Python-based integrations.
This ensures that data remains connected to the models it helps develop. Consequently, data preparation, AI training, testing, and deployment become interconnected parts of the same workflow.
One platform instead of isolated tools
The development of Surgical AI brings together different disciplines: surgeons and medical experts, annotators, researchers, data scientists and software developers work together. While each group has different requirements and perspectives, they all depend on reliable data and clearly defined processes.
Chimaera ImViA brings these perspectives together in a shared workflow:
The key difference is not just annotation. Chimaera ImViA supports the entire lifecycle of surgical video data, from raw footage to quality-assured, AI-ready datasets, and from the development and validation of AI models to their application.
This provides a structured foundation for:
- clinical research and studies,
- surgical documentation and quality assessment,
- education and training,
- multicenter research projects,
- the development and evaluation of AI-based applications.
The connected path to Surgical AI
Surgical video data is most valuable when it can be transformed into reliable, traceable information. A single layer of annotation is insufficient. What is needed is a workflow that connects data, domain expertise, quality assurance, and AI development.
Chimaera ImViA unites this entire process in a central, browser-based platform – from the initial annotation to validated AI.
Discover how surgical video data can be structured, quality-assured and made usable for AI-based applications.
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