Myopia Detection from Eye Fundus Images: New Screening Method Based on You Only Look Once Version 8 †

Abstract

Myopia is an eye disorder of global concern due to its increasing prevalence worldwide and its potential to cause sight-threatening conditions. Diagnosis is based on clinical tests such as objective cycloplegic refraction, distance visual acuity, and axial length measurements. Population-based screening is an early detection method that helps prevent uncorrected vision disorders. Advancements in technology and artificial intelligence (AI) applications in the medical field are improving the speed and efficiency of patient care programs. In an effort to provide a new, objective AI-based method for early myopia detection, we developed an algorithm based on the YOLOv8 convolutional neural network, capable of classifying eye fundus images from myopic and non-myopic patients. Preliminary results from an image set obtained from an Italian optometric practice show an overall accuracy of 85.00% and a precision and recall of 88.7% and 91.7%, respectively, in the internal validation dataset. This represents the beginning of a new paradigm, where AI is central to large screening programs aimed at preventing myopia and other avoidable blinding conditions and enabling early diagnosis and management. © 2024 by the authors. --//-- This is an open-access article Rizzieri, N.; Dall’Asta, L.; Ozoliņš, M. Myopia Detection from Eye Fundus Images: New Screening Method Based on You Only Look Once Version 8. Appl. Sci. 2024, 14, 11926. https://doi.org/10.3390/app142411926 published under the CC BY 4.0 licence.