Experimental protocol designed to employ Nd:YAG laser surgery for anterior chamber glaucoma detection via UBM

Saba Ghazanfar Ali, Riaz Ali, Bin Sheng, Yan Chen, Huating Li, Po Yang, Ping Li, Younhyun Jung, Fang Zhu, Ping Lu, Jinman Kim

Research output: Journal article publicationJournal articleAcademic researchpeer-review

Abstract

Angle closure glaucoma leads to fluid deposition in eye, and intraocular pressure occurs that damage the optic nerve, causes blindness and vision loss. Anterior chamber (AC) evaluation is imperative for determining the risk of angle-closure. Previously, techniques were dependent on either Pentacam–Scheimpflug that interprets poor visual information, anterior segment optical coherence tomography is injurious to intercede opaque optical structures. Therefore, in this paper, an experimental protocol is designed for detailed disease analysis based on IBM SPSS statistics via ultrasound biomicroscopy which is superior in evaluating deep structures; first, the affected parameter for AC is analysed, and afterwards the direction that needs laser surgery is explored. Experiments are conducted on large-scale clinical studies from an affiliated hospital in Shanghai, China. The dataset comprised 600 AC images in five directions of 60 subjects. The mean with standard deviation for anterior open distance is 0.15879 (Formula presented.) 0.096779 mm, 0.15863 (Formula presented.) 0.081435 mm, and anterior chamber angle is 18.749 (Formula presented.) 08.0315 (Formula presented.), 18.741 (Formula presented.) 08.3889 (Formula presented.) for left and right eye respectively. It is found that anterior chamber angle in the downside of the AC is wider than the upside. However, this decision is partly based on the narrowest part of the angle to widen the depth of the direction and eliminate pupil block.

Original languageEnglish
Pages (from-to)2171-2179
Number of pages9
JournalIET Image Processing
Volume16
Issue number8
DOIs
Publication statusPublished - Jun 2022

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Computer Vision and Pattern Recognition
  • Electrical and Electronic Engineering

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