Migration & Analysis of ECG Images
About the Customer & Challenges Faced:
A healthcare provider based in Pennsylvania is using our data lake solution with image processing capabilities. The solution is helping them extract metadata and pixel information from echocardiogram (ECG) images (DICOM) stored across fragmented data storages, making them available for search and analysis. The solution has enabled real-time access to patient demographic data, textual information, measurement values, and DICOM image data.
Solution and Approach:
- Some major enablers towards large-scale adoption of AI-ML practices and precision CV-based medicine, available through xpresso.ai, include dynamic availability of numerous analytics algorithms, models and methods in a pull-down type of menu, easy management of important issues like data ownership, governance and standards, continuous data acquisition and data cleansing.
- xpressso.ai framework provides out-of-the-box development platforms. The project was started seamlessly with the relevant environments which were then created automatically. Development images configured based on pre-defined templates were installed on-premises or in a development VM within the xpresso.ai infrastructure. This enabled authentication using LDAP, seamless project setup using Bitbucket, Jenkins and Docker (ensuring build and deployment without software compatibility issues).
- The framework made available by xpresso.ai leverages the latest ML and DL tools while preparing models and includes Pachyderm-based data versioning, deployment using a Kubernetes orchestration system, Kubeflow and Spark-based ML and DL build and deployment, Istio-based service mesh enabled microservice architecture, and ELK based monitoring capability; contributing to reduction in latency time.
- The platform can potentially be fed with input data (relational data for patient demographics, text notes and image data in DICOM standard).
- xpresso.ai’s MLOps framework allows establishing high-end Alluxio and Presto-based efficient data connectivity and collecting data from diverse sources. xpresso.ai was used to migrate data to an integrated data lake based on Hadoop. The solution comprises xpresso.ai Big Data components including Apache Solr as the federated search platform, seamlessly integrated into this data lake. xpresso.ai image processing components that use ML-based algorithms were employed to generate relevant metadata for each ECG image. The solution enabled quick and informative image retrieval from the integrated Hadoop data lake.
- The details collected were added as exploratory variables by using xpresso.ai libraries and analyzed. The attributes obtained were used for categorization (employing Pachyderm-based data versioning) and then performing univariate, bi-variate and Bag of Words analysis — for both structured and unstructured datasets through xpresso Exploratory Data Analysis (Data and Statistical Analysis). Different datasets and their different versions were easily controlled and stored into xpresso Data Model (XDM)-enabled data store that enabled easy retrieval and storage of datasets/ files into internal XDM. This was achieved by using two excellent features of xpresso.ai:
- Data Connectivity Marketplace libraries
- Data Versioning
- Finally, we were able to process huge volumes of image metadata and scale up by almost 70%, enabling our clients and end-users to derive quicker insights from ECG images.
- By using xpresso.ai, one can leverage high-end data connectivity, efficient data versioning, perform exploratory data analysis and generate inferences using an intuitive process and through an industry-standardized manner.
- The unique, containerized platform-centric approach offered by xpresso.ai can be used to employ required infrastructure, deploy rapidly to multiple high-availability environments while aligning with best-in-class DevSecOps practices.
- xpresso.ai also brings in-depth QA-QC testing and logging frameworks, synchronous and asynchronous monitoring, and performance tracking ability.
- xpresso.ai also has SSO (single-sign-on) for various in-built tools and subsystems that make the platform access seamless throughout.
- In a nutshell, all the above features in a single plate under the same hood make xpresso.ai an unbeatable AI Ops framework.
Have Any Questions?
Need more information about the platform?