Artificial intelligence in the diagnosis and management of refractive errors.

Journal: European journal of ophthalmology
Published Date:

Abstract

Refractive error is among the leading causes of visual impairment globally. The diagnosis and management of refractive error has traditionally relied on comprehensive eye examinations by eye care professionals, but access to these specialized services has remained limited in many areas of the world. Given this, artificial intelligence (AI) has shown immense potential in transforming the diagnosis and management of refractive error. We review AI applications across various aspects of refractive error care - from axial length prediction using fundus images to risk stratification for myopia progression. AI algorithms can be trained to analyze clinical data to detect refractive error as well as predict associated risks of myopia progression. For treatments such as implantable collamer and orthokeratology lenses, AI models facilitate vault size prediction and optimal lens fitting with high accuracy. Furthermore, AI has demonstrated promise in optimizing surgical planning and outcomes for refractive procedures. Emerging digital technologies such as telehealth, smartphone applications, and virtual reality integrated with AI present novel avenues for refractive error screening. We discuss key challenges, including limited validation datasets, lack of data standardization, image quality issues, population heterogeneity, practical deployment, and ethical considerations regarding patient privacy that need to be addressed before widespread clinical implementation.

Authors

  • Tuan Nguyen
  • Joshua Ong
  • Venkata Jonnakuti
    Medical Scientist Training Program, Baylor College of Medicine, Houston, Texas, USA.
  • Mouayad Masalkhi
    University College Dublin School of Medicine, Belfield, Dublin 4, Ireland.
  • Ethan Waisberg
    University College Dublin School of Medicine, Belfield, Dublin, Ireland. Electronic address: ethan.waisberg@ucdconnect.ie.
  • Sarah Aman
    Wilmer Eye Institute, Johns Hopkins Medicine, Baltimore, MD, United States.
  • Nasif Zaman
    Human-Machine Perception Laboratory, Department of Computer Science and Engineering, University of Nevada, Reno, Reno, Nevada, United States.
  • Prithul Sarker
    Human-Machine Perception Laboratory, Department of Computer Science and Engineering, University of Nevada, Reno, Reno, NV, USA.
  • Zhen Ling Teo
    Singapore Eye Research Institute, Singapore National Eye Centre, Singapore.
  • Daniel S W Ting
    Singapore National Eye Center, Duke-National University of Singapore Medical School, Singapore 168751, Singapore; National Institutes of Health Research Biomedical Research Centre Biomedical Centre, Moorfields Eye Hospital NHS Foundation Trust and UCL Institute of Ophthalmology, London, UK. Electronic address: daniel.ting.s.w@singhealth.com.sg.
  • Darren S J Ting
    Academic Unit of Ophthalmology, Institute of Inflammation and Ageing, University of Birmingham, Birmingham, United Kingdom.
  • Alireza Tavakkoli
    Department of Computer Science and Engineering, University of Nevada School of Medicine, Reno, NV 89557, USA.
  • Andrew G Lee
    Center for Space Medicine, Baylor College of Medicine, Houston, Texas, United States; Department of Ophthalmology, Blanton Eye Institute, Houston Methodist Hospital, Houston, Texas, United States; The Houston Methodist Research Institute, Houston Methodist Hospital, Houston, Texas, United States; Departments of Ophthalmology, Neurology, and Neurosurgery, Weill Cornell Medicine, New York, New York, United States; Department of Ophthalmology, University of Texas Medical Branch, Galveston, Texas, United States; University of Texas MD Anderson Cancer Center, Houston, Texas, United States; Texas A&M College of Medicine, Texas, United States; Department of Ophthalmology, The University of Iowa Hospitals and Clinics, Iowa City, Iowa, United States.