Create Surgical Video AI Models 6x Faster With Encord

Build better surgical intelligence models with Encord’s video labeling and clinical operations platform.

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Our Groundbreaking Healthcare Customers

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Collaboratively Label and Manage Surgical Video Training Data with Encord

Encord is a medical-grade video annotation and clinical operations platform designed for surgical intelligence use cases, including surgical robotics and AI-assisted surgery.

Encord makes video labeling quicker and more efficient, reducing the hours your annotation team needs to spend labeling and reviewing images. And Encord’s advanced labeling protocols and data quality features ensure the best training data is being used to train your surgical intelligence models.

Develop your surgical AI models faster and more efficiently with Encord.

Efficient Medical Grade Image Labeling

Encord supports rich labeling protocols within its surgical video annotation tool.

You can create complex labeling protocols covering as many feature types as you need to deliver the right video training data to your machine learning teams.

A Single Platform for Surgical Video Annotation

Encord offers multiple ways to annotate videos within a single platform, including bounding box, polygon, polyline, keypoint, segmentation and classification.

This means your annotators and reviewers only have to use a single piece of software to do their jobs. And your developers don’t have to try and maintain multiple in-house tools to cover each type of annotation task.

Segmentation of brain in CT scan

The Industry’s Most Powerful Labeling Protocols

Give your annotators a single platform for all their annotation types, including bounding box, polygon, polyline, keypoint, segmentation and classification.

And with a single platform, your developers are saved from maintaining multiple custom or open-source annotation tools.

Expert review quality control for radiology

Efficient Clinical Data Operations

Encord has been designed to make it easier for you to meet your regulatory submissions, including FDA and CE approval.

In addition to being HIPAA and SOC-2 compliant, Encord’s data pipelines, auditable labels and QC and QA features make regulatory compliance much less of a headache.

Encord improves the quality of your radiology training data

Create Better Surgical Video Training Data

Encord helps clinical operations teams identify the errors, biases and imbalances in the datasets they’ve created (even down to individual annotator performance).

This allows clinical operations leaders to provide better datasets to their machine learning teams and help reduce model time to production.

Encord supports FDA clearance and CE marking processes

Supporting Your Regulatory Journey

Encord has been designed to make it easier for you to meet your regulatory requirements, including FDA submissions and CE approval.

As well as being HIPAA and SOC2compliant, Encord’s data pipelines, fully auditable labels and quality control and assurance features make regulatory compliance much less of a headache.

Customer Stories

Read how our customers are using Encord to accelerate the development of their healthcare machine learning models

MSK logo
Memorial Sloan Kettering Cancer Center

MSK adopted Encord to build custom label protocols for pulmonary thrombosis projects.

Detecting and classifying vena cava filters in complex label protocols (ontologies) rendered existing & open-source tools unusable.
Deployed Encord's label protocol studio to build custom protocols, DICOM annotation tool, worklists & automation modules to increase efficiency.
Project made feasible by the flexibility offered by Encord's ontology study.
Protocol configurations
10 minutes
Total setup time

Stanford Medicine
Stanford Medicine

The Division of Nephrology reduced experiment duration by 80% while processing 3x more images.

Stanford was using three different pieces of software to identify, annotate, and count podocytes and glomeruli in microscopy images.
Stanford started using Encord's annotation tools & SDK to automate segmentations, count, and calculate sizes of segments.
With Encord, Stanford researchers reduced experiment duration from an average of 21 to 4 days while processing 3x the number of images.
Reduction in experiment duration
Number of images
1 platform
... and not 3

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