Universität zu Köln

Li, Lei, Zhu, Haogang, Zhang, Zhenyu, Zhao, Liang, Xu, Liang, Jonas, Rahul A., Garway-Heath, David F., Jonas, Jost B. and Wang, Ya Xing (2021). Neural Network-Based Retinal Nerve Fiber Layer Profile Compensation for Glaucoma Diagnosis in Myopia: Model Development and Validation. JMIR Med. Inf., 9 (5). TORONTO: JMIR PUBLICATIONS, INC. ISSN 2291-9694

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Abstract

Background: Due to the axial elongation-associated changes in the optic nerve and retina in high myopia, traditional methods like optic disc evaluation and visual field are not able to correctly differentiate glaucomatous lesions. It has been clinically challenging to detect glaucoma in highly myopic eyes. Objective: This study aimed to develop a neural network to adjust for the dependence of the peripapillary retinal nerve fiber layer (RNFL) thickness (RNFLT) profile on age, gender, and ocular biometric parameters and to evaluate the network's performance for glaucoma diagnosis, especially in high myopia. Methods: RNFLT with 768 points on the circumferential 3.4-mm scan was measured using spectral-domain optical coherence tomography. A fully connected network and a radial basis function network were trained for vertical (scaling) and horizontal (shift) transformation of the RNFLT profile with adjustment for age, axial length (AL), disc-fovea angle, and distance in a test group of 2223 nonglaucomatous eyes. The performance of RNFLT compensation was evaluated in an independent group of 254 glaucoma patients and 254 nonglaucomatous participants. Results: By applying the RNFL compensation algorithm, the area under the receiver operating characteristic curve for detecting glaucoma increased from 0.70 to 0.84, from 0.75 to 0.89, from 0.77 to 0.89, and from 0.78 to 0.87 for eyes in the highest 10% percentile subgroup of the AL distribution (mean 26.0, SD 0.9 mm), highest 20% percentile subgroup of the AL distribution (mean 25.3, SD 1.0 mm), highest 30% percentile subgroup of the AL distribution (mean 24.9, SD 1.0 mm), and any AL (mean 23.5, SD 1.2 mm), respectively, in comparison with unadjusted RNFLT. The difference between uncompensated and compensated RNFLT values increased with longer axial length, with enlargement of 19.8%, 18.9%, 16.2%, and 11.3% in the highest 10% percentile subgroup, highest 20% percentile subgroup, highest 30% percentile subgroup, and all eyes, respectively. Conclusions: In a population-based study sample, an algorithm-based adjustment for age, gender, and ocular biometric parameters improved the diagnostic precision of the RNFLT profile for glaucoma detection particularly in myopic and highly myopic eyes.

Item Type:	Journal Article
Creators:	Creators Email ORCID ORCID Put Code Li, Lei UNSPECIFIED UNSPECIFIED UNSPECIFIED Zhu, Haogang UNSPECIFIED UNSPECIFIED UNSPECIFIED Zhang, Zhenyu UNSPECIFIED UNSPECIFIED UNSPECIFIED Zhao, Liang UNSPECIFIED UNSPECIFIED UNSPECIFIED Xu, Liang UNSPECIFIED UNSPECIFIED UNSPECIFIED Jonas, Rahul A. UNSPECIFIED UNSPECIFIED UNSPECIFIED Garway-Heath, David F. UNSPECIFIED UNSPECIFIED UNSPECIFIED Jonas, Jost B. UNSPECIFIED UNSPECIFIED UNSPECIFIED Wang, Ya Xing UNSPECIFIED UNSPECIFIED UNSPECIFIED
URN:	urn:nbn:de:hbz:38-592040
DOI:	10.2196/22664
Journal or Publication Title:	JMIR Med. Inf.
Volume:	9
Number:	5
Date:	2021
Publisher:	JMIR PUBLICATIONS, INC
Place of Publication:	TORONTO
ISSN:	2291-9694
Language:	English
Faculty:	Unspecified
Divisions:	Unspecified
Subjects:	no entry
Uncontrolled Keywords:	Keywords Language OPEN-ANGLE GLAUCOMA; DIABETIC-RETINOPATHY; LEARNING ALGORITHM; VISION IMPAIRMENT; AXIAL LENGTH; PREVALENCE; RISK; PERFORMANCE; PARAMETERS; BLINDNESS Multiple languages Medical Informatics Multiple languages
URI:	http://kups.ub.uni-koeln.de/id/eprint/59204