Utilization of deep learning to quantify fluid volume of neovascular age-related macular degeneration patients based on swept-source OCT imaging: The ONTARIO study.
Utilization of deep learning to quantify fluid volume of neovascular age-related macular degeneration patients based on swept-source OCT imaging: The ONTARIO study.
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DOI：
10.1371/journal.pone.0262111
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发表时间：
2022
期刊：
影响因子：
3.7
通讯作者：
Choudhry N
中科院分区：
文献类型：
--
作者：
Sodhi SK;Pereira A;Oakley JD;Golding J;Trimboli C;Russakoff DB;Choudhry N
评价基于深度学习的算法的预测能力，以使用基线扫频源光学相干断层扫描（SS-OCT）和OCT血管造影（OCT-A）数据确定新生血管性年龄相关性黄斑变性（nARMD）患者的长期最佳矫正远视力（BCVA）结局。在这项IV期、回顾性、概念验证、单中心研究中，来自17只既往接受过治疗的nARMD患眼的SS-OCT数据用于评估视网膜层厚度，并使用基于深度学习的新型黄斑流体分割算法量化视网膜内液（IRF）、视网膜下液（SRF）和浆液性色素上皮细胞脱膜（PED）。使用Pearson相关系数（PCC）将基线OCT和OCT-A形态学特征和液体测量值与基线至第52周BCVA的变化相关联。基线时的总视网膜液（IRF、SRF和PED）体积与第12个月BCVA改善的相关性最强（PCC = 0.652，p = 0.005）。随后将液体细分为IRF、SRF和PED，PED体积与BCVA改善的相关性仅次于IRF（PCC = 0.648，p = 0.005）。孤立的平均总视网膜厚度显示出较差的相关性（PCC = 0.334，p = 0.189）。当两个特征，平均脉络膜新生血管膜（CNVM）的大小和总液体量，相结合，并与视力结果，最高的相关性增加到PCC = 0.695（p = 0.002）。单独而言，总液体量与基线和第52周之间BCVA值的变化最密切相关。结合OCT-A的补充信息，观察到线性相关评分改善。平均总视网膜厚度提供了较低的相关性，因此提供了比评估的替代度量更低的预测结果。在临床上，结合病变大小分析液体指标的机器学习方法可以在个性化治疗和预测第52周的BCVA结果方面提供优势。
To evaluate the predictive ability of a deep learning-based algorithm to determine long-term best-corrected distance visual acuity (BCVA) outcomes in neovascular age-related macular degeneration (nARMD) patients using baseline swept-source optical coherence tomography (SS-OCT) and OCT-angiography (OCT-A) data. In this phase IV, retrospective, proof of concept, single center study, SS-OCT data from 17 previously treated nARMD eyes was used to assess retinal layer thicknesses, as well as quantify intraretinal fluid (IRF), subretinal fluid (SRF), and serous pigment epithelium detachments (PEDs) using a novel deep learning-based, macular fluid segmentation algorithm. Baseline OCT and OCT-A morphological features and fluid measurements were correlated using the Pearson correlation coefficient (PCC) to changes in BCVA from baseline to week 52. Total retinal fluid (IRF, SRF and PED) volume at baseline had the strongest correlation to improvement in BCVA at month 12 (PCC = 0.652, p = 0.005). Fluid was subsequently sub-categorized into IRF, SRF and PED, with PED volume having the next highest correlation (PCC = 0.648, p = 0.005) to BCVA improvement. Average total retinal thickness in isolation demonstrated poor correlation (PCC = 0.334, p = 0.189). When two features, mean choroidal neovascular membranes (CNVM) size and total fluid volume, were combined and correlated with visual outcomes, the highest correlation increased to PCC = 0.695 (p = 0.002). In isolation, total fluid volume most closely correlates with change in BCVA values between baseline and week 52. In combination with complimentary information from OCT-A, an improvement in the linear correlation score was observed. Average total retinal thickness provided a lower correlation, and thus provides a lower predictive outcome than alternative metrics assessed. Clinically, a machine-learning approach to analyzing fluid metrics in combination with lesion size may provide an advantage in personalizing therapy and predicting BCVA outcomes at week 52.
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DOI：
10.3928/23258160-20140909-08
发表时间：
2014-09
期刊：
Ophthalmic surgery, lasers & imaging retina
影响因子：
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作者：
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DOI：
10.1097/iae.0000000000001447
发表时间：
2017-10-01
影响因子：
3.3
作者：
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