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首页> 外文期刊>JMIR Medical Informatics >Neural Network–Based Retinal Nerve Fiber Layer Profile Compensation for Glaucoma Diagnosis in Myopia: Model Development and Validation
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Neural Network–Based Retinal Nerve Fiber Layer Profile Compensation for Glaucoma Diagnosis in Myopia: Model Development and Validation

机译：基于神经网络的视网膜神经纤维层轮廓补偿近视的青光眼诊断：模型开发和验证

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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.
机译：背景技术由于高近视的视神经和视网膜中的轴向伸长率相关变化，传统方法如视镜盘评估和视野等不能正确地区分青光瘤病变。在高度近视眼中检测青光眼一直在临床上挑战。目的本研究旨在开发一个神经网络，以调整围毛动物视网膜神经纤维层（RNFL）厚度（RNFLT）厚度（RNFLT）型概况在年龄，性别和眼部生物识别参数上的依赖性，并评估网络对青光眼诊断的性能，特别是在高近视。方法使用光谱域光学相干断层扫描测量圆周3.4mm扫描上的768点的RNFLT。具有垂直（缩放）和水平（移位）变换的完全连接的网络和径向基函数网络，其调整为年龄，轴向长度（Al），磁盘 - FOVEA角度和测试组中的距离2223神道的眼睛。在254名青光眼患者和254名非农宫参与者的独立组中评估了RNFLT补偿的性能。通过应用RNFL补偿算法，接收器的接收器处的区域，用于检测青光眼增加0.75至0.89，从0.77〜0.89增加到0.77〜0.89，在最高10％百分位数中为0.78至0.87的眼睛Al分布（平均26.0，SD 0.9 mm），Al分布的最高20％百分位子组（平均25.3，SD 1.0 mm），Al分布的最高30％百分位子组（平均24.9，SD 1.0 mm），以及任何与未经调整的RNFLT相比，Al（平均23.5，SD 1.2 mm）分别。未补偿和补偿的RNFLT值之间的差异随着较长的轴向长度而增加，增大19.8％，18.9％，16.2％，最高10％百分位数，最高的20％百分位数，最高的30％百分位子组，和11.3％所有的眼睛分别。结论在基于人群的研究样本中，年龄，性别和眼部生物识别参数的基于算法的调整改善了rnflt曲线的诊断精度，以进行青光眼检测，特别是在近视和高度近视眼中。

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