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There were 975 medical device recalls in the United States in 2023, resulting in 283 million recalled units as well as significant financial and reputation loss.
Improve the Precision and Reliability of Automated Vision Systems to Prevent Defect Escapes and Faulty Shipments.
Manual device inspections are susceptible to human error, and classical vision systems that are rule-based don’t yield well for process variations. As a result, defective devices end up in patients’ hands.
Leverage Akridata’s Inspection Studio to build and deploy reliable inspection models that are based on state-of-the-art deep learning and transformer architectures. Can work with your existing hardware.
Don’t have an in-house Data Science team for device defect detection?
Akridata Edge provides ready-to-use medical device models that have been rigorously tested and refined using millions of device images, ensuring accurate defect detection.
A medical device client sought to improve accuracy and efficiency in its computer vision-based inspection lines. The company's existing system fell short despite repeated model tuning. Real-world production conditions and a rigid, speed-focused approach caused incorrect part flagging, posing the challenge of maintaining speed without sacrificing accuracy or risking defective product shipments.
Leveraging Akridata Inspection Studio, the company achieved a 40% decrease in false positives and a 30% reduction in false negatives, significantly reducing inventory wastage and safeguarding the brand’s esteemed reputation.