CitiusTech has leveraged AI/ML and deep learning technologies to build an Ingestion and Metadata Management Framework for enhanced Image Analytics that can help decipher images, support screening tests, disease diagnosis and predict disease progression.

Traditionally, clinicians and researchers have relied on DICOM imaging standard for workflow and imaging data management. However, medical imaging has expanded beyond order-based imaging for patients to other ologies like ophthalmology, dermatology, digital pathology, and other encounter-based images where non-DICOM images are prevalent.

CitiusTech’s ingestion framework and metadata enhancement solution that will streamline:

• Search/query mechanism to build a cohort of required set of imaging data for research and annotation purposes

• Labelling of imaging data using structured codes, anatomical region, and therapeutic areas for research study cataloguing

• Integration of imaging data with associated clinical data

Advantages:

• Robust search for multimodality, multi format imaging data

• Platform agonistic HL7 FHIR framework for metadata management throughout the lifecycle of data

• Customizable framework for metadata enhancement based on targeted research

Key Healthcare Use cases:

• Development of Imaging Platform for Secondary Analysis

• Standardized Uptake Value (SUV) Measurements in Image Viewer

• Imaging Analytics for Disease Risk Quantification

• Interoperability for Imaging Devices

Case Study:

Client is a global medical device company and a leader in providing solutions for orthopedics. Client wanted to establish interoperability between its image management solutions and RIS, PACS and other hospital applications. CitiusTech developed a standards-based solution to enable extensibility to other applications and interoperability between client applications and RIS, PACS, and other hospital information systems.