Data DayChest x-ray TubeLocateMay 27th, 2021

Dheeraj Boda (OHIA –Solution Architecture and Engineering)

Introduction and the Ask

• Design and implement a solution to Productionalize the Real time image processing Application and Models using scalable, efficient and robust processes and infrastructure

• Have DR and HA enabled secure environments for image processing• Have automated roundtrip processing time less than 4 min• Store logging and intermediate resulting information, make available for

further investigation, enhancements

Enabled Azure Services • Azure Storage• Azure Private Endpoints• Azure Key Vault• Azure Event Grid• Azure Functions• Azure Batch Service• Azure Kubernetes Service• Azure SQL DB• Azure Application Insights• Azure Machine Learning service

Future Enhancements with XOpsXOps is an umbrella term used to define and integrate various aspects of automating machine learning lifecycle that includes various IT disciplines and responsibilities which could include Development, deployment, security, networking etc. with IT operations to be secure, reliable and efficiently maintained.

DevSecOps

1.Data Pipelines2.Environment Pipelines3.Model Training Pipelines4.Model Deployment

Pipelines5.Azure ML Service (MLflow)6.Azure Databricks (MLflow)

MLOps

Questions

UCLA Data Week

Real-Time Intelligence System for Detection of Tubes and Lines in Chest X-Rays

Matthew Brown, PhDCenter for Computer Vision and Imaging Biomarkers

UCLA PACS Datcard RouterCVIBSENN

Microsoft Azure

CareConnect

2) CXR image 3) CXR image

5) Original CXR + Enhanced CXR with AI annotations and alerts

4) DICOM image with AI overlay

UCLA Clinical DeploymentX-ray Unit

1) CXR image

4) DICOM image with AI overlay

IRB for R&D

Train and test AI

FDA 510(k)? No

QI Project? Yes

Integration

Legal Review

Risk Assessment

Rollout – Phase 1

Rollout – Phase 2

Pilot users• Selected physicians that place orders• Selected ACI radiologists• New order number

Weekly meeting during pilot phase

Data set: 1,900 annotated chest x-rays• 1396 training, 514 test

Detection accuracy: 96% ET, 94% NG

Mean ET tip error: 7mm• 86% < 10 mm