In reality, an ophthalmic DICOM does exist, but it is used by few, and many OCT manufacturers continue to utilize closed formats that require users to “pay extra” for DICOM capabilities.

Surely we can make our OCTs, visual fields, and autorefractometers talk to each other and feed into an electronic health record system? It is therefore incongruous that this hard-won interoperability seems a lot easier in radiology than in ophthalmology. We are at the height of the digital age, as compared with our radiology colleagues who broached this problem when the World Wide Web was merely a toddler.

Ophthalmologists now face a similar challenge but in a very different time. DICOM (as with any industry standard) has its limitations, however it has created an interoperable and open system that has been central to advancements in the field of radiology. DICOM was first co-developed in 1985 by the American College of Radiology and the National Electrical Manufacturers Association, yet it took almost 10 years to take its current form today an almost universally accepted industry communications standard for medical images. This era saw the development of Digital Imaging and Communications in Medicine (DICOM) – a means of standardizing medical imaging and patient information across different devices. However, the disruptive technology of computerized tomography (CT) and magnetic resonance imaging (MRI) scans that all but made single frame X-rays obsolete, proved a tipping point for the field. Something as simple as an OCT image cannot be read using the proprietary platforms provided by the different OCT manufacturers – and in many cases, the imaging data is encrypted! This is reminiscent of the situation that radiology was in more than 30 years ago where radiologists using different imaging devices could not decode or share their images. One of the main barriers holding us back is legacy technology. It also creates more work – if there’s any ambiguity at all, you absolutely need to perform your own OCT scans and retinal photographs. These archaic methods of communication is not only insufficient for effective triage, it renders the pursuit of collaborative care or new technology adoption invalid. Occasionally, referrals may come to an ophthalmologist with a poor quality image – often, as a black and white grainy printout, or a single snapshot of an OCT scan (as opposed to the 100 image volume scan of the macula), or worse, a smartphone photo of a scan acquired by the patient or a relative. Incompatible file formats and image management systems along that patient journey can mean that there is no easy way for ophthalmologists to quickly access high-quality image scans. But as they move along their patient journey, it seems that no-one is sharing their eye images. In a typical workflow, a patient will see an optometrist or a general practitioner (GP) – or both – before being referred to an ophthalmologist.

Arguably, this utopia is within our reach today, but understanding its barriers and identifying practical solutions will be key in realizing this in our day-to-day practice. A remotely located expert in the relevant ophthalmic subspecialty could then validate this report, communicate health advice to the patient and/or advise the need for a face-to-face encounter or repeat scan. In an ideal setting, an image will be acquired in a place convenient to the patient and an artificial intelligence algorithm will read the scans and generate an instantaneous report for the patient. A natural starting point for this, I believe, will be in the field of teleophthalmology, which relies heavily on both acquiring images of the eye and an electronic means to review, report, and relay information to the patient.

However, a crucial ingredient for the successful integration of these technical advances into the real world of clinical practice is the existence of platforms that accommodate them, and the foresight of building infrastructure to support these promising technologies. Advances in imaging technology are revealing more about ocular disease, and the combination of “big data” and artificial intelligence strategies (such as Moorfields and Google DeepMind Health’s recent collaboration) is set to transform the diagnosis and detection of disease from images of the eye. It’s an exciting time to be working in ophthalmology.

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