机构：[1]State Key Laboratory of Software Development Environment, School of Computer Science and Engineering, Beihang University, Beijing, China[2]Beijing Institute of Ophthalmology, Beijing Tongren Hospital, Capital University of Medical Science, Beijing Ophthalmology and Visual Sciences Key Laboratory, Beijing, China研究所眼科研究所首都医科大学附属北京同仁医院首都医科大学附属同仁医院[3]NIHR Biomedical Research Centre for Ophthalmology, Moorfields Eye Hospital NHS Foundation Trust, UCL Institute of Ophthalmology, London, United Kingdom[4]Department of Ophthalmology, Faculty of Medicine and University Hospital, University of Cologne, Cologne, Germany[5]Department of Ophthalmology, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
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.
基金：
National Natural Science Foundation of ChinaNational Natural Science Foundation of China (NSFC) [81570835]; State Key Laboratory of Software Development Environment, Beihang University, Beijing, China
第一作者机构：[1]State Key Laboratory of Software Development Environment, School of Computer Science and Engineering, Beihang University, Beijing, China[2]Beijing Institute of Ophthalmology, Beijing Tongren Hospital, Capital University of Medical Science, Beijing Ophthalmology and Visual Sciences Key Laboratory, Beijing, China
通讯作者：
通讯机构：[2]Beijing Institute of Ophthalmology, Beijing Tongren Hospital, Capital University of Medical Science, Beijing Ophthalmology and Visual Sciences Key Laboratory, Beijing, China[*1]Beijing Institute of Ophthalmology Beijing Tongren Hospital, Capital University of Medical Science Beijing Ophthalmology and Visual Sciences Key Laboratory 17 Hougou Lane Beijing, 100005 China
推荐引用方式(GB/T 7714)：
Li Lei,Zhu Haogang,Zhang Zhenyu,et al.Neural Network-Based Retinal Nerve Fiber Layer Profile Compensation for Glaucoma Diagnosis in Myopia: Model Development and Validation[J].JMIR MEDICAL INFORMATICS.2021,9(5):doi:10.2196/22664.
APA：
Li, Lei,Zhu, Haogang,Zhang, Zhenyu,Zhao, Liang,Xu, Liang...&Wang, Ya Xing.(2021).Neural Network-Based Retinal Nerve Fiber Layer Profile Compensation for Glaucoma Diagnosis in Myopia: Model Development and Validation.JMIR MEDICAL INFORMATICS,9,(5)
MLA：
Li, Lei,et al."Neural Network-Based Retinal Nerve Fiber Layer Profile Compensation for Glaucoma Diagnosis in Myopia: Model Development and Validation".JMIR MEDICAL INFORMATICS 9..5(2021)
