Skip to main content

EDITORIAL article

Front. Med.
Sec. Ophthalmology
Volume 11 - 2024 | doi: 10.3389/fmed.2024.1523647
This article is part of the Research Topic Efficient Artificial Intelligence (AI) in Ophthalmic Imaging View all 10 articles

Editorial: Efficient Artificial Intelligence (AI) in Ophthalmic Imaging

Provisionally accepted
Yanda Meng Yanda Meng 1,2*Meng Wang Meng Wang 3Haoyu Chen Haoyu Chen 4,5Yalin Zheng Yalin Zheng 2
  • 1 University of Exeter, Exeter, United Kingdom
  • 2 University of Liverpool, Liverpool, North West England, United Kingdom
  • 3 National University of Singapore, Singapore, Singapore
  • 4 Shantou University, Shantou, Guangdong Province, China
  • 5 The Chinese University of Hong Kong, Shatin, Hong Kong Region, China

The final, formatted version of the article will be published soon.

    Wang et al. (2023) proposed a new automated framework driven by recent advances in deep learning 13 to extract 12 three-dimensional parameters from the automatically segmented hyperreflective foci in 14 optical coherence tomography (OCT). This is because the fast and automated reconstruction of retinal 15 hyperreflective foci (HRF) is of great importance for understanding many eye-related diseases. Retinal vessels play a pivotal role as biomarkers in detecting retinal diseases, including hypertensive 17 retinopathy. The manual identification of these retinal vessels is both resource-intensive and time- Axial length (AL) is a widely discussed parameter, significant not only for defining the eye's refractive 44 status but also due to its strong association with retinal and macular complications. The excessive

    Keywords: Efficient AI, ophthalmic imaging, Retinal, ocular, OCT, Colour fundus

    Received: 06 Nov 2024; Accepted: 02 Dec 2024.

    Copyright: © 2024 Meng, Wang, Chen and Zheng. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence: Yanda Meng, University of Exeter, Exeter, United Kingdom

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.