The American Academy of Ophthalmology (AAO), backed by the Royal Australian and New Zealand College of Ophthalmologists, has called on ophthalmic imaging device manufacturers to standardise image formats in accordance with the Digital Imaging and Communications in Medicine (DICOM) standard.
Since 1993, DICOM has promoted the seamless sharing of all medical images by detailing how to format them and the information they contain, such as descriptions and patient information. Compliance with the standard is, however, low for ophthalmic devices, with even those claiming to be ‘DICOM compliant’ not fully meeting DICOM specifications, said the AAO, adding that currently there is no easy way to exchange digital imaging data from one manufacturer’s equipment to another’s without a custom interface.
Dr Aaron Lee, lead author on the AAO’s report into this area, said the lack of standardisation in ophthalmology is hindering the profession. “The new horizon of tools for digital healthcare rely on being able to interact algorithmically and extract data at scale. Right now, there has been great progress with electronic health record data becoming standardised and available directly to patients, but the same has not yet happened for imaging and functional testing data that we routinely collect in our clinics.” DICOM alignment would allow eyecare providers better access to images and reports, which supports faster and more coordinated patient care, he said.
The report cites two specific examples of how ophthalmologists would benefit from compliant devices:
- Currently, when a patient’s OCT image or visual field test is stored, there is no way to bring all the data to one place to inform clinical decision making. This could be remedied by machine-readable, discrete data for user selected reports of ophthalmic imaging or functional testing
- Use of lossless compression for pixel or voxel data to encode the same raw data as used by manufacturers. When an imaging device or functional testing device captures data, it is often compressed to make the files smaller, which can degrade image quality. Poor image quality can also lead to problems when AI models are being developed or new digital health tools are deployed
