Data entry ops · Production

Intuitive Surgical doubles labeled dataset delivery speed for robotic surgery ML models using Labelbox

The problem

Intuitive Surgical's data science team was bottlenecked by the need to annotate tens of thousands of surgical videos frame-by-frame — a tedious, time-consuming process — while also ensuring all labeled data adhered to a consistent ontology across clinical and data science teams.

Workflow diagram · grounded in source
1
Surgical video annotation need
trigger
“Annotating bounding boxes frame-by-frame in tens of thousands of videos is a tedious and time consuming process, because a large variety of surgical tools and surgeries must be captured for robust model training”
2
Video annotation in native editor
human_review
“using the native video editor to annotate their unstructured data”
3
Model-assisted labeling
ai_action
“speed up annotation workflows between domain experts and labelers using model-assisted labeling”
4
Labeling velocity and quality measurement
validation
“measure labeling velocity & efficiency with detailed metrics”
5
Labeled dataset delivery for ML models
output
“double the speed at which they can deliver labeled datasets for their multiple ML models”
Reported outcome

By adopting Labelbox, Intuitive Surgical doubled the speed of labeled dataset delivery for their ML models, scaled labeling efficiency and throughput, and lowered overhead for gathering quality and performance metrics.

Reported metrics
Labeled dataset delivery speeddoubling the speed
Labeling efficiency and throughputscale their labeling efficiency and throughput
Metrics gathering overheadlowering their overhead in gathering metrics on performance and quality
Reported stack
LabelboxAnnotate
Source
https://labelbox.com/customers/intuitive-surgical-customer-story
Read source ↗

Frequently asked questions

What did this team achieve with this AI workflow?

By adopting Labelbox, Intuitive Surgical doubled the speed of labeled dataset delivery for their ML models, scaled labeling efficiency and throughput, and lowered overhead for gathering quality and performance metrics.

What tools did this team use?

Labelbox, Annotate.

What results were reported?

Labeled dataset delivery speed: doubling the speed; Labeling efficiency and throughput: scale their labeling efficiency and throughput; Metrics gathering overhead: lowering their overhead in gathering metrics on performance and quality (source-reported, not independently verified).

How is this data entry ops AI workflow structured?

Surgical video annotation need → Video annotation in native editor → Model-assisted labeling → Labeling velocity and quality measurement → Labeled dataset delivery for ML models.

Related data entry ops cases