Postsurgery Classification of Best-Corrected Visual Acuity Changes Based on Pterygium Characteristics Using the Machine Learning Technique.

Journal: TheScientificWorldJournal
PMID: 34819813

Abstract

INTRODUCTION: Early detection of visual symptoms in pterygium patients is crucial as the progression of the disease can cause visual disruption and contribute to visual impairment. Best-corrected visual acuity (BCVA) and corneal astigmatism influence the degree of visual impairment due to direct invasion of fibrovascular tissue into the cornea. However, there were different characteristics of pterygium used to evaluate the severity of visual impairment, including fleshiness, size, length, and redness. The innovation of machine learning technology in visual science may contribute to developing a highly accurate predictive analytics model of BCVA outcomes in postsurgery pterygium patients.

Authors

  • Fatin Nabihah Jais
    Kulliyyah of Allied Health Sciences, International Islamic University Malaysia, Bandar Indera Mahkota, Kuantan 25200, Pahang, Malaysia.
  • Mohd Zulfaezal Che Azemin
    Kulliyyah of Allied Health Sciences, International Islamic University Malaysia, Bandar Indera Mahkota, Kuantan 25200, Pahang, Malaysia.
  • Mohd Radzi Hilmi
    Kulliyyah of Allied Health Sciences, International Islamic University Malaysia, Bandar Indera Mahkota, Kuantan 25200, Pahang, Malaysia.
  • Mohd Izzuddin Mohd Tamrin
    Kulliyyah of ICT, International Islamic University Malaysia, Gombak, Kuala Lumpur 50728, Malaysia.
  • Khairidzan Mohd Kamal
    Kulliyyah of Medicine, International Islamic University Malaysia, Bandar Indera Mahkota, Kuantan 25200, Pahang, Malaysia.

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