AI-Assisted Quality Control of CTD Data

Use Case Objectives

• Machine Learning Task: Flag in advance the scans to be deleted during CTD quality control
• Business Value: Flagged scans allow the analyst to quickly focus attention on crucial areas, reducing the time and effort required to delete scans
• Measures of Success:

  * Accuracy of model predictions
  * Client feedback on quality control speed-ups

• Aspirational Goals:

* Mitigation of uncertainty in human decisions
* Semi or full automation of scan deletions

Machine Learning Pipeline

Experimental Model Performance

Model Deployment and Integration