Comprehensive Summary
This paper looks at the use of deep learning systems (DLS) to identify optic neuropathies, specifically looking at Papilledema, using a handheld camera. This model was developed using 20,533 retinal, undialated photographs. These photos were then classified with their optic disc abnormalities, analyzing different aspects of the photographs to better evaluate diagnostic outcomes. The DLS was seen to have a good performance, having a diagnostic accuracy of 99.5%, showing an ability to differentiate papilledema from controls, as well as other conditions. This shows that when provided with high quality images of dialated eyes, handheld, low-res images are still able to be analyzed with high accuracy. This can improve prospects of rapid screening outside of a clinical setting. The paper mainly connects the integration of AI with clinical diagnosis, giving viable alternatives to resource-deficient environemnts.
Outcomes and Implications
The improvement and integration of DLS into ophthalmology care can improve diagnosis speeds, make healthcare more accesible, and provide a tool to confirm preexisting conditions. By integrating this technology into handheld cameras, it gives the ability to diagnose conditions in an out of clinic setting. For papilledema especially, this can be very important as an early diagnosis can catch life threatening rises in intracranial pressure. While traditional detection requires specialized equipment, DLS provides alternatives available to a greater amount of people. While current models do have a high level of accuracy when it comes to papilledema diagnosis, further integration into other programs can expand the scope of healthcare. Over the next couple years, with validation and regulatory processes being developed, these tools could be implemented clinically.