quality_assurance · healthcare · workflow
Intuitive Surgical doubles labeled dataset delivery speed for surgical ML models with Labelbox
Intuitive Surgical's data science team faced a bottleneck in obtaining sufficient labeled video data to train ML models for surgical instrument detection, with frame-by-frame bounding-box annotation across tens of thousands of videos being tedious and time-consuming, and a requirement to maintain a consistent ontology across clinical and data science teams.
How it works
Common implementation structure
How this type of workflow is generally built, generalized across documented cases — not tied to any one vendor's stack. Click any stage to read what happens there. Specific products that implement these stages appear in “Tools commonly seen” below.
Stage 1 · Surgical video annotation need
Annotating bounding boxes frame-by-frame in tens of thousands of surgical videos is a tedious and time-consuming process requiring capture of a large variety of surgical tools and surgeries.
Tools used
LabelboxAnnotatenative video editormodel-assisted labeling
Outcome
Intuitive Surgical doubled the speed at which they deliver labeled datasets for their ML models, scaled labeling efficiency and throughput, and lowered overhead in gathering performance and quality metrics.
Grounding & classification
Source type: vendor customer story
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computer visiondata extractionmedical recordhuman review describedmetric backednamed customertools describedvendor confirmedhealthcareemployee productivitythroughput increasetime savedvendor customer storydata entry opsquality assuranceai draft human approvalhuman review queue