Saturday, May 10, 2025
5/10/2025
Increasing Interoperability of Brain Morphometrics Using FHIR - PROJECT SUMMARY With the rise of artificial intelligence (AI) algorithms in medicine, radiologists have new tools at their disposal to quantitatively assess imaging data. However, in order to unlock this potential, data needs to be shared easily and effectively between all parts of the health information technology (IT) system. The goal of this project is to reduce data access barriers by developing software to cleanly integrate medical imaging data stored in a radiology department’s picture archiving and communication systems (PACS) with the rest of patients’ electronic health record (EHR) using the Fast Healthcare Interoperability Resources (FHIR®) standard. CorticoMetrics will use our THINQ™ software as a medical device (SaMD) product to provide brain morphometrics derived from MR imaging data, and extend its functionality to output results in both Digital Imaging and Communications in Medicine structured reporting (DICOM-SR) and Health Level 7 Fast Healthcare Interoperability Resources (HL7 FHIR) compliant formats. Based off of the scientifically validated FreeSurfer suite of automated neuroimaging analysis software, THINQ provides measurements of brain structures that can aid in the care of neurological conditions such as Alzheimer's disease and dementia, traumatic brain injury, epilepsy, hydrocephalus, Parkinson's disease and multiple sclerosis. Output in FHIR and DICOM-SR formats will be validated and included in CorticoMetrics’ next FDA 510(k) submission of THINQ. Incorporating this information with the rest of the rest of a patient’s EHR will enable a seamless workflow for clinicians to make decisions more efficiently and accurately while also improving the performance of those with less experience. This project will develop and disseminate an open source software tool to interconvert neuroimaging data between formats used in academic settings (such as FreeSurfer’s MGH or Neuroimaging Informatics Technology Initiative (NIfTI)) with the standard formats used in health care settings (DICOM and FHIR). Common Data Elements (CDE) will be used to facilitate data sharing across studies where appropriate. The product will lead to an increase in interoperability of brain morphometrics, giving medical professionals access to key data directly in the EHR. While THINQ will serve as an initial use case of this technology, the conversion tool will be easily extensible to other use cases, and freely available to developers of the next generation of quantitative imaging software.
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