Linking AI Initiatives to Real-World Outcomes

Use Case

IMAGE MIGRATION: EFFICIENT ECHOCARDIOGRAM (ECG) DATA ANALYSIS

 
Client

Large healthcare provider based in Pennsylvania.

Problem

The client faced difficulties accessing massive volumes of image metadata. They wanted to retrieve and analyze ECG data, which was being stored in fragmented data storage with inadequate metadata.

 

Methodology
  • Combination of Deep Learning based professional service and modularized AI components used according to organization’s cognitive maturity
  • Business transformation with smarter cognitive outcomes
  • Adding industry, business and customer insights to the value chain
  • Seamlessly integrate data across functions
Solution
  • The solution comprised of Big Data components that includes Apache Solr as the federated search platform, seamlessly integrated to a data lake
  • Migrated data to an integrated data lake based on Hadoop
  • The solution generated image processing components that use Machine Learning are employed to generate relevant metadata for each ECG image
  • Enabled quick and informative image retrieval from the integrated Hadoop data lake
Benefits:
  • 70% increased efficiency in processing huge volumes of image metadata
  • Enabled end users to derive quick insights from ECG images
 

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