An Artificial Intelligence Enabled System for Retinal Nerve Fiber Layer Thickness Damage Severity Staging.
An Artificial Intelligence Enabled System for Retinal Nerve Fiber Layer Thickness Damage Severity Staging.
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DOI：
10.1016/j.xops.2023.100389
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发表时间：
2024-03
影响因子：
--
通讯作者：
Johnson, Chris
中科院分区：
文献类型：
--
作者：
Yousefi, Siamak;Huang, Xiaoqin;Poursoroush, Asma;Majoor, Julek;Lemij, Hans;Vermeer, Koen;Elze, Tobias;Wang, Mengyu;Nouri-Mahdavi, Kouros;Mohammadzadeh, Vahid;Brusini, Paolo;Johnson, Chris
目的：建立一个基于OCT视网膜神经纤维层（RNFL）厚度的客观青光眼损伤严重程度分级系统。基于多中心OCT数据的RNFL损伤严重度分类算法开发。1171例受试者2269只眼的6561个视乳头周围RNFL轮廓用于建立模型，900例受试者1099只眼的2505个RNFL轮廓用于验证模型。我们开发了一个无监督的k-means模型来识别具有相似RNFL厚度分布的眼睛集群。我们根据它们各自的全局RNFL厚度对聚类进行了注释。根据贝叶斯最小误差原理计算了区分不同严重程度的最佳全局RNFL厚度阈值。我们基于一个独立的验证数据集验证了所提出的管道，该数据集包含来自900名受试者的1099只眼睛的2505个RNFL配置文件。准确度、受试者工作特征曲线下面积和混淆矩阵。k-means聚类发现了4个聚类，分别具有1382、1613、1727和1839个样本，平均（标准差）全局RNFL厚度为58.3（8.9）μm、78.9（6.7）μm、87.7（8.2）μm和101.5（7.9）μm。贝叶斯最小误差分类器分别识别出> 95、86至95、70至85和< 70的最佳全局RNFL值，用于区分正常眼睛和处于RNFL厚度损失的早期、中度和晚期阶段的眼睛。约4%的正常眼和98%的晚期RNFL缺失眼的RNFL厚度超出OCT仪器提供的正常范围。无监督机器学习发现，用于区分正常眼睛和早期、中度和晚期RNFL损失的眼睛的最佳RNFL阈值分别为95、85 μm和70。这个RNFL损失分类系统是公正的，因为在开发过程中没有预设或人类专家干预。此外，它是客观的，易于使用和一致的，这可能会增加青光眼研究和日常临床实践。专有或商业披露可以在本文末尾的脚注和披露中找到。
To develop an objective glaucoma damage severity classification system based on OCT-derived retinal nerve fiber layer (RNFL) thickness measurements. Algorithm development for RNFL damage severity classification based on multicenter OCT data. A total of 6561 circumpapillary RNFL profiles from 2269 eyes of 1171 subjects to develop models, and 2505 RNFL profiles from 1099 eyes of 900 subjects to validate models. We developed an unsupervised k-means model to identify clusters of eyes with similar RNFL thickness profiles. We annotated the clusters based on their respective global RNFL thickness. We computed the optimal global RNFL thickness thresholds that discriminated different severity levels based on Bayes’ minimum error principle. We validated the proposed pipeline based on an independent validation dataset with 2505 RNFL profiles from 1099 eyes of 900 subjects. Accuracy, area under the receiver operating characteristic curve, and confusion matrix. The k-means clustering discovered 4 clusters with 1382, 1613, 1727, and 1839 samples with mean (standard deviation) global RNFL thickness of 58.3 (8.9) μm, 78.9 (6.7) μm, 87.7 (8.2) μm, and 101.5 (7.9) μm. The Bayes’ minimum error classifier identified optimal global RNFL values of > 95 , 86 to 95 , 70 to 85 and < 70 for discriminating normal eyes and eyes at the early, moderate, and advanced stages of RNFL thickness loss, respectively. About 4% of normal eyes and 98% of eyes with advanced RNFL loss had either global, or ≥ 1 quadrant, RNFL thickness outside of normal limits provided by the OCT instrument. Unsupervised machine learning discovered that the optimal RNFL thresholds for separating normal eyes and eyes with early, moderate, and advanced RNFL loss were 95 , 85 μm, and 70 , respectively. This RNFL loss classification system is unbiased as there was no preassumption or human expert intervention in the development process. Additionally, it is objective, easy to use, and consistent, which may augment glaucoma research and day-to-day clinical practice. Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.
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影响因子：
0.4
作者：
Elbendary, Amal M.;Helal, Randa Mohamed
通讯作者：
Helal, Randa Mohamed
影响因子：
4.1
作者：
Foster, PJ;Buhrmann, R;Johnson, GJ
通讯作者：
Johnson, GJ
影响因子：
2
作者：
Brusini, Paolo;Zeppieri, Marco;Salvetat, Maria Letizia
通讯作者：
Salvetat, Maria Letizia
影响因子：
4.2
作者：
QUIGLEY, HA;DUNKELBERGER, GR;GREEN, WR
通讯作者：
GREEN, WR
影响因子：
--
作者：
Keltner, JL;Johnson, CA;Gordon, MO
通讯作者：
Gordon, MO