How MMI Accelerates Digital Surgery Innovation with Encord

The Challenge: Scaling Computer Vision for Robotic Microsurgery

MMI is building the future of digital and robotic microsurgery. Their work sits at the intersection of robotics, computer vision, and AI, supporting surgeons who operate at an extremely small scale where precision, tremor reduction, and motion control are critical.

To power these systems, MMI relies on large volumes of high-quality annotated video data. But microsurgical data presents unique challenges: it comes from multiple sources, requires extreme precision, and must be curated and validated carefully to ensure stable model performance across environments.

As their computer vision efforts scaled, manual data handling and annotation became a bottleneck. Engineers needed to focus on model optimisation, testing, and innovation, not spending disproportionate time curating datasets, reviewing annotations, or managing fragmented workflows.

MMI needed a solution that could:

• Scale annotation and review efficiently
• Support complex video-based computer vision workflows
• Integrate tightly with their existing AI pipelines
• Improve dataset consistency and model stability
• Reduce the time from hypothesis to model validation

The Solution: Encord as the Backbone of MMI’s Data & Annotation Workflow

MMI adopted Encord as a core part of their computer vision and data operations, using it across annotation, review, data curation, and automation.

With Encord, the team gained access to:

• An external annotation workforce, allowing engineers to prioritise model innovation over manual labeling
• Workflow automation and model-assisted labeling, accelerating annotation and review cycles
• Systematic review policies and targeted relabeling, improving dataset consistency and edge-case handling
• Encord Index, enabling efficient browsing, categorisation, and reuse of large-scale surgical video data
• SDK integrations, allowing seamless export into MMI’s internal training pipelines

Engineers across roles, from senior CV engineers to PhD researchers and interns, use Encord daily to review annotations, visualise edge cases, automate parts of the workflow, and move faster from experimentation to validation.

The Results: Faster Iteration, Better Data, More Focus on Innovation

By integrating Encord into their AI-driven workflows, MMI significantly improved both operational efficiency and model quality.

Annotation and review cycles became faster and more consistent, allowing the team to iterate on multiple model versions more quickly. Improved dataset quality translated directly into more stable model performance across different test environments.

Most importantly, Encord freed up valuable engineering time. Instead of manually handling data, MMI’s team can focus on what matters most: advancing the state of the art in digital surgery, developing assistive and autonomous capabilities, and delivering better outcomes for surgeons and patients.

As MMI continues to expand its digital surgery platforms, including first-in-human deployments, Encord remains a critical partner in enabling scalable, reliable, and high-quality computer vision development.