The research aims to demonstrate a code-free deep learning method for detecting papilledema in clinical settings.
Developed a user-friendly deep learning model for optic disc imaging.
Tested the model's effectiveness in recognizing papilledema and pseudopapilledema.
Engaged clinicians without coding expertise to validate the model's usability.
The deep learning model accurately identifies papilledema and pseudopapilledema.
Clinicians found the tool accessible and easy to use without prior coding experience.
Abstract
Automated machine learning can be feasibly used to provide an accessible, scalable solution for clinical teams without coding expertise to recognize and characterize papilledema.