Diagnostic Accuracy of Macular Thickness Map and Texture En Face Images for Detecting Glaucoma in Eyes With Axial High Myopia.
Diagnostic Accuracy of Macular Thickness Map and Texture En Face Images for Detecting Glaucoma in Eyes With Axial High Myopia.
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黄斑厚度图和纹理 En 面部图像用于检测轴性高度近视眼青光眼的诊断准确性。
DOI：
10.1016/j.ajo.2022.04.019
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发表时间：
2022
影响因子：
4.2
通讯作者：
Zangwill,LindaM
中科院分区：
文献类型：
--
作者：
Bowd,Christopher;Belghith,Akram;Rezapour,Jasmin;Christopher,Mark;Hyman,Leslie;Jonas,JostB;Weinreb,RobertN;Zangwill,LindaM
目的评价一种新的基于光学相干断层扫描纹理的人脸图像分析方法(SALSA纹理)在轴性高度近视眼青光眼诊断中的准确性，并将SALSA纹理与标准黄斑神经节细胞-内丛状层厚度、黄斑视网膜神经纤维层厚度和神经节细胞复合体厚度图进行比较。方法收集92例原发性开角型青光眼(POAG)和44例正常对照眼轴性高度近视的横断面数据。光学相干层析成像纹理人脸图像是从位于内界膜玻璃边界下方的70μm薄板上生成的，该图像使用Salsa纹理来模拟区域内像素强度的空间排列模式。结果经参数调整的AUROCs(95%可信区间)对正常眼和青光眼高度近视眼的鉴别为0.92(0.88~0.94)，黄斑RNFL厚度0.88(0.86~0.91)，黄斑GCIPL厚度0.87(0.83~0.89)，GCC厚度为0.87(0.84~0.89)。高度近视眼的亚集分析(眼轴长度≥27 mm；3 8只青光眼和2 2只健康眼)的AUROC值分别为0.92(0.89~0.94)、0.86(0.84~0.88)、0.86(0.84~0.88)、0.86(0.84~0.88)和0.84(0.81~0.87)(P<0.0 2)。结论本文提出的基于纹理的黄斑组织厚度分析方法可以提高现有基于纹理的黄斑组织厚度测量方法对高度近视青光眼和高度近视健康眼的鉴别诊断能力。结论基于纹理的黄斑组织厚度分析方法能够有效地区分高度近视青光眼和高度近视健康眼。结论基于纹理的黄斑组织厚度分析方法能够有效地区分高度近视青光眼和高度近视健康眼。结论基于纹理的黄斑组织厚度分析方法能够有效地区分高度近视青光眼和高度近视健康眼。结论基于纹理的黄斑组织厚度分析方法能够有效地区分高度近视青光眼和高度近视健康眼。结论基于纹理的黄斑组织厚度分析方法能够有效地区分高度近视青光眼和高度近视健康眼。结论基于纹理的黄斑组织厚度分析方法能够有效地区分高度近视青光眼和高度近视健康眼。结论基于纹理的黄斑组织厚度分析方法能够有效地区分高度近视青光眼和高度近视健康眼。虽然还需要进一步的研究，但人脸纹理图像显示出改善高度近视眼睛青光眼检测的希望，而传统的视网膜层分割通常是具有挑战性的。
PurposeTo evaluate the diagnostic accuracy of a novel optical coherence tomography texture-based en face image analysis (SALSA-Texture) that requires segmentation of only 1 retinal layer for glaucoma detection in eyes with axial high myopia, and to compare SALSA-Texture with standard macular ganglion cell–inner plexiform layer (GCIPL) thickness, macular retinal nerve fiber layer (mRNFL) thickness, and ganglion cell complex (GCC) thickness maps.DesignComparison of diagnostic approaches.MethodsCross-sectional data were collected from 92 eyes with primary open-angle glaucoma (POAG) and 44 healthy control eyes with axial high myopia (axial length >26 mm). Optical coherence tomography texture en face images, developed using SALSA-Texture to model the spatial arrangement patterns of the pixel intensities in a region, were generated from 70-μm slabs just below the vitreal border of the inner limiting membrane. Areas under the receiver operating characteristic curves (AUROCs) and areas under the precision recall curves (AUPRCs) adjusted for both eyes, axial length, age, disc area, and image quality were used to compare different approaches.ResultsThe best parameter-adjusted AUROCs (95% confidence intervals) for differentiating between healthy and glaucoma high myopic eyes were 0.92 (0.88-0.94) for texture en face images, 0.88 (0.86-0.91) for macular RNFL thickness, 0.87 (0.83-0.89) for macula GCIPL thickness, and 0.87 (0.84-0.89) for GCC thickness. A subset analysis of highly advanced myopic eyes (axial length ≥27 mm; 38 glaucomatous eyes and 22 healthy eyes) showed the best AUROC was 0.92 (0.89-0.94) for texture en face images compared with 0.86 (0.84-0.88) for macular GCIPL, 0.86 (0.84-0.88) for GCC, and 0.84 (0.81-0.87) for RNFL thickness (P≤ .02 compared with texture for all comparisons).ConclusionThe current results suggest that our novel en face texture-based analysis method can improve on most investigated macular tissue thickness measurements for discriminating between highly myopic glaucomatous and highly myopic healthy eyes. While further investigation is needed, texture en face images show promise for improving the detection of glaucoma in eyes with high myopia where traditional retinal layer segmentation often is challenging.
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