Swift Cost-Effectively Scaled Annotations Without Compromising Accuracy

The Overview

The Swift Skin and Wound mobile app brings AI to the bedside to help patients and clinicians monitor wound health. Swift partnered with Sama to cost-effectively scale annotations for their CV model, without compromising the clinician-level accuracy they needed.

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The Challenge

The Swift AutoTissue CV model determines tissue types present within a wound. To provide wound measurement and diagnostics that rivaled or even surpassed the accuracy of trained physicians, Swift Medical needed high-quality annotation at scale.

In-house labeling was too expensive to scale, and outsourced labeling partners could not meet quality requirements. Swift Medical needed a labeling partner who could be upskilled to accurately label a variety of wound tissue types and bounds with clinician-level precision.

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The Solution

Swift needed images segmented by different wound area regions and labels for wound type. To ensure clinician-level quality, Sama introduced:

Specialized, in-depth annotator training
Bespoke training from Swift’s clinical team to upskill Sama annotators. 30% subset of dataset labeled, validated, before scaling to the full dataset.

Continuous feedback loops
Slack, email, and phone to align on edge cases and iterate on instructions, resulting in decreased time per task and increased task complexity in the workflow.

Rigorous quality control
Multi-step QA process: AutoQA, dedicated QA team, with Swift sampling weekly and providing feedback to continuously improve quality and throughput.

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