About the Medical Diagnosis pattern
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.
Ingest chest X-rays from a simulated X-ray machine and puts them into an
objectStorebased on Ceph.
objectStoresends 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.
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
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.
Components are running on OpenShift either at the data center, at the medical facility, or public cloud running OpenShift.
Getting started Deploy the Pattern