Korean J Ophthalmol.  2019 Apr;33(2):103-112. 10.3341/kjo.2018.0060.

Clinical Efficacy of Custom-built Software for the Early Detection of Glaucoma: A Comparison of Axial-length and Major Retinal Artery Location Data

• Jang H
• Lee SM
• Ahn J
• Rho S

• KMID: 2442614
• DOI: http://doi.org/10.3341/kjo.2018.0060

Abstract

PURPOSE
To assess the clinical efficacy for early detection of glaucoma using custom-built image software visualizing translucent retinal nerve fiber layer thickness (RNFLT) that is graphed based on a normative database.
METHODS
This prospective study was conducted using a normative database constructed with RNFLT data of 151 healthy Korean eyes. The reference lines of the mean, the lower 5%, and the lower 1% limit were visualized as a translucent RNFLT graph produced by our software after inputting each subject's major retinal artery position and overlaying the results onto the RNFLT measurements. Fifty-eight additional healthy control and 79 early-glaucoma eyes were collected for the validation group. If a subject's RNFLT graph was outside the reference line of the lower 1% limit, the graph was defined as abnormal. The lower 1% limit, which was generated by three criteria (criterion 1, built-in software; criterion 2, axial-length data; criterion 3, major retinal artery data), was used to address the difference of agreement with a standard answer.
RESULTS
For criteria 1, 2, and 3, the accuracy of our custom-built software was significantly higher than that of the manufacturer's database (kappa of 0.475 vs. 0.852 vs. 0.940; sensitivity of 62.0% vs. 91.1% vs. 97.5%, respectively) maintaining high specificity (87.9% vs. 94.8% vs. 96.6%, respectively).
CONCLUSIONS
The custom-built imaging software with the constructed RNFLT normative database showed high clinical efficiency for early detection of glaucoma with negligible user-related variability.

Keyword

Axial length; Database; Glaucoma; Major retinal artery position; Optical coherence tomography

MeSH Terms

Glaucoma*
Nerve Fibers
Prospective Studies
Retinal Artery*
Retinaldehyde*
Sensitivity and Specificity
Tomography, Optical Coherence
Treatment Outcome*
Retinaldehyde

Figure

• 

  Fig. 1 A demonstration of an overlaying function using our custom-built software. (A) The main window of the custom-built software shows the principal features including database management, artery information input window, and the graph visualization function key. (B,C) The background was seen using a manual adjustment cursor.

• 

  Fig. 2 A representative case of early glaucoma detection using our custom-built software. (A) A red-free photograph shows a localized retinal nerve fiber layer (RNFL) defect (arrows) in the inferotemporal region. (B) Early damage of the superior visual field was noted in the subject's right eye that corresponded to the inferotemporal RNFL defect. (C) The inferotemporal RNFL defect was not detected by the built-in software; (D) however, the defect was detected by our custom-built software. GHT = glaucoma hemifield test; VFI = visual-field index; MD = mean deviation; PSD = pattern standard deviation.

• 

  Fig. 3 A representative discordance case shows the detection ability of criterion 3 in the early diagnosis of glaucoma. (A) Red-free photography shows a wedge-shaped retinal nerve fiber layer (RNFL) defect in the inferotemporal region. The short red arrows indicate the margin of the defect. (B) Pattern deviation and global indices of visual-field tests demonstrate outside normal limit defects in the superonasal quadrant. (C) Criterion 1 (top) and criterion 2 (middle) cannot detect any RNFL defect based on the lower 1% limit of RNFL thickness. Only criterion 3 (bottom) detects the glaucomatous RNFL defect (long red arrow). GHT = glaucoma hemifield test; VFI = visual-field index; MD = mean deviation; PSD = pattern standard deviation.

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