Validated Patterns

Medical Diagnosis

Validation status:
Validated
CI status:
Links:

About the Medical Diagnosis pattern

Background

This validated pattern is based on a demo implementation of an automated data pipeline for chest X-ray analysis that was previously developed by Red Hat. You can find the original demonstration here. It was developed for the US Department of Veteran Affairs.

This validated pattern includes the same functionality as the original demonstration. The difference is that this solution uses the GitOps framework to deploy the pattern including Operators, creation of namespaces, and cluster configuration. Using GitOps provides an efficient means of implementing continuous deployment.

Workflow
  • Ingest chest X-rays from a simulated X-ray machine and puts them into an objectStore based on Ceph.

  • The objectStore sends a notification to a Kafka topic.

  • A KNative Eventing listener to the topic triggers a KNative Serving function.

  • An ML-trained model running in a container makes a risk assessment of Pneumonia for incoming images.

  • A Grafana dashboard displays the pipeline in real time, along with images incoming, processed, anonymized, and full metrics collected from Prometheus.

This pipeline is showcased in this video.

dashboard

About the solution elements

The solution aids the understanding of the following:

  • How to use a GitOps approach to keep in control of configuration and operations.

  • How to deploy AI/ML technologies for medical diagnosis using GitOps.

The Medical Diagnosis pattern uses the following products and technologies:

  • Red Hat OpenShift Container Platform for container orchestration

  • Red Hat OpenShift GitOps, a GitOps continuous delivery (CD) solution

  • Red Hat AMQ, an event streaming platform based on the Apache Kafka

  • Red Hat OpenShift Serverless for event-driven applications

  • Red Hat OpenShift Data Foundation for cloud native storage capabilities

  • Grafana Operator to manage and share Grafana dashboards, data sources, and so on

  • S3 storage

About the architecture

Presently, the Medical Diagnosis pattern does not have an edge component. Edge deployment capabilities are planned as part of the pattern architecture for a future release.

edge medical diagnosis marketing slide

Components are running on OpenShift either at the data center, at the medical facility, or public cloud running OpenShift.

About the physical schema

The following diagram shows the components that are deployed with the various networks that connect them.

physical network

The following diagram shows the components that are deployed with the the data flows and API calls between them.

physical dataflow

Recorded demo

Demo</a>

Next steps