A Novel Approach To Predict Glaucomatous Impairment in the Central 10° Visual Field, Excluding the Effect of Cataract.

IF 2.6 3区医学 Q2 OPHTHALMOLOGY

Translational Vision Science & Technology Pub Date : 2024-10-01 DOI:10.1167/tvst.13.10.35

Ryo Tomita, Ryo Asaoka, Kazunori Hirasawa, Yuri Fujino, Tetsuro Omura, Tsutomu Inatomi, Akira Obana, Koji M Nishiguchi, Masaki Tanito

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引用次数: 0

Abstract

Purpose: Our previous study predicted genuine glaucomatous visual field (VF) impairment in the central 10° VF, excluding the effect of cataract, using visual acuity (VA) and global indexes of VF more accurately than pattern deviation (PD). This study aimed to improve the accuracy by using pointwise total deviation (TD) values with the machine-learning method of random forest model (RFM) and to investigate whether incorporating optical coherence tomography-measured ganglion cell-inner plexiform layer (GCIPL) thickness is useful.

Methods: This retrospective study included 89 eyes with open-angle glaucoma that underwent successful cataract surgery (with or without iStent implantation or ab interno trabeculotomy). Postoperative TD in each of the 68 VF points was predicted using preoperative (1) PD, (2) VA and VF with a linear regression model (LM), and (3) VA and VF with RFM, and averaged as predicted mean TD (mTDpost). Further prediction was made by incorporating the preoperative GCIPL into the best model.

Results: The mean absolute error (MAE) between the actual and predicted mTDpost with RFM (1.25 ± 1.03 dB) was significantly smaller than that with PD (3.20 ± 4.06 dB, p < 0.01) and LM (1.42 ± 1.06 dB, p < 0.05). The MAEs with the model incorporating GCIPL into RFM (1.24 ± 1.04 dB) and RFM were not significantly different.

Conclusions: Accurate prediction of genuine glaucomatous VF impairment was achieved using pointwise TD with RFM. No merit was observed by incorporating the GCIPL into this model.

Translational relevance: This pointwise RFM could clinically reduce cataract effect on VF.

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排除白内障影响，预测中央 10° 视野青光眼损害的新方法。

目的：与模式偏差（PD）相比，我们之前的研究使用视力（VA）和VF的全局指数更准确地预测了中央10°VF的真正青光眼视野（VF）损害，排除了白内障的影响。本研究旨在利用随机森林模型（RFM）的机器学习方法，通过使用点状总偏差（TD）值来提高准确性，并探讨将光学相干断层扫描测量的神经节细胞内丛状层（GCIPL）厚度纳入其中是否有用：这项回顾性研究纳入了89只成功接受白内障手术（植入或未植入iStent或小梁切开术）的开角型青光眼患者。使用术前（1）PD、（2）线性回归模型（LM）预测VA和VF，以及（3）RFM预测VA和VF，预测68个VF点的术后TD，并平均为预测平均TD（mTDpost）。通过将术前 GCIPL 纳入最佳模型，进一步进行预测：结果：RFM（1.25 ± 1.03 dB）和 LM（1.42 ± 1.06 dB，p < 0.05）的实际和预测 mTDpost 之间的平均绝对误差（MAE）明显小于 PD（3.20 ± 4.06 dB，p < 0.01）和 LM（1.42 ± 1.06 dB，p < 0.05）。将 GCIPL 纳入 RFM 的模型（1.24 ± 1.04 dB）和 RFM 的 MAE 没有显著差异：结论：使用点式 TD 和 RFM 可以准确预测真正的青光眼 VF 损伤。结论：使用点式 TD 和 RFM 可以准确预测真正的青光眼 VF 损伤，将 GCIPL 纳入该模型没有发现任何优点：这种点式 RFM 可以在临床上减少白内障对 VF 的影响。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Translational Vision Science & Technology Engineering-Biomedical Engineering

CiteScore

5.70

自引率

3.30%

发文量

346

审稿时长

25 weeks

期刊介绍： Translational Vision Science & Technology (TVST), an official journal of the Association for Research in Vision and Ophthalmology (ARVO), an international organization whose purpose is to advance research worldwide into understanding the visual system and preventing, treating and curing its disorders, is an online, open access, peer-reviewed journal emphasizing multidisciplinary research that bridges the gap between basic research and clinical care. A highly qualified and diverse group of Associate Editors and Editorial Board Members is led by Editor-in-Chief Marco Zarbin, MD, PhD, FARVO. The journal covers a broad spectrum of work, including but not limited to: Applications of stem cell technology for regenerative medicine, Development of new animal models of human diseases, Tissue bioengineering, Chemical engineering to improve virus-based gene delivery, Nanotechnology for drug delivery, Design and synthesis of artificial extracellular matrices, Development of a true microsurgical operating environment, Refining data analysis algorithms to improve in vivo imaging technology, Results of Phase 1 clinical trials, Reverse translational ("bedside to bench") research. TVST seeks manuscripts from scientists and clinicians with diverse backgrounds ranging from basic chemistry to ophthalmic surgery that will advance or change the way we understand and/or treat vision-threatening diseases. TVST encourages the use of color, multimedia, hyperlinks, program code and other digital enhancements.