George Magrath, Joseph Luvisi, Daniel Russakoff, Jonathan Oakley, Emil Say, Jeffrey Blice, Ashwath Jayagopal, Sally Tucker, Alex Loayza, George Hamilton Baker, Jihad S Obeid
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引用次数: 0
摘要
目的:利用卷积神经网络(CNN),通过光学相干断层扫描(OCT)数据,预测未经治疗的糖尿病黄斑水肿(DME)患者对单次注射抗血管内皮生长因子(anti-VEGF)的反应:方法:通过病历回顾进行回顾性研究:设置:这是一项在南卡罗来纳医科大学斯托姆眼科研究所(Storm Eye Institute)进行的单中心研究:新诊断为DME并接受玻璃体内(IVT)抗血管内皮生长因子注射的患者,只要在诊断时进行过基线OCT扫描,并在首次注射抗血管内皮生长因子后进行过1个月的随访OCT扫描,即可纳入研究。排除标准包括之前接受过抗血管内皮生长因子治疗、未进行必要的 OCT 扫描、并存黄斑变性以及其他视网膜疾病引起的黄斑水肿。共纳入了 53 名患者的 73 只眼睛:基线检查的 OCT 扫描结果与首次注射抗血管内皮生长因子约 1 个月后的随访 OCT 扫描结果进行比较,以确定中央子场厚度(delta CST)的变化。将 delta CST 作为标签输入 CNN,以训练系统仅根据基线 OCT 扫描结果预测治疗反应:主要结果指标:CNN 对抗 VEGF 治疗反应的预测。治疗反应被定义为 CST 降低 10 µm 或更多:结果:根据两次 OCT 扫描的 CST 值,57 只眼睛对最初的抗血管内皮生长因子注射有反应,16 只眼睛无反应。仅分析每只眼睛的基线 OCT 扫描,训练有素的 CNN 的曲线下面积 (AUC) 为 0.81。在报告的操作点上,CNN 正确识别了 57 只反应眼中的 45 只(即召回率为 78.9%)和 16 只非反应眼中的 11 只(即特异性为 68.8%):这项研究结果证明了 CNN 预测治疗无效的 DME 对单次注射抗血管内皮生长因子疗法的反应的潜力。
Use of a Convolutional Neural Network to Predict the Response of Diabetic Macular Edema to Intravitreal Anti-VEGF Treatment: A Pilot Study.
Purpose: To utilize a convolutional neural network (CNN) to predict the response of treatment-naïve diabetic macular edema (DME) to a single injection of anti-vascular endothelial growth factor (anti-VEGF) with data from optical coherence tomography (OCT).
Design: Retrospective study performed via chart review.
Methods: Setting: This was a single-center study performed at the Storm Eye Institute, Medical University of South Carolina.
Patient population: Patients with a new diagnosis of DME who underwent intravitreal (IVT) anti-VEGF injections were eligible for inclusion, provided they had a baseline OCT scan at the time of diagnosis and a 1-month follow-up OCT scan after the first anti-VEGF injection. Exclusion criteria included prior treatment with anti-VEGF, lack of required OCT scans, coexistent macular degeneration, and macular edema due to other retinal diseases. Seventy-three (73) eyes from 53 patients were included.
Intervention: The OCT scan from the baseline visit was compared to the follow-up OCT scan approximately 1 month after the first anti-VEGF injection to determine change in central subfield thickness (delta CST). The delta CST was fed into the CNN as a label to train the system to predict treatment response from only the baseline OCT scan.
Main outcome measure: CNN prediction of treatment response to anti-VEGF. Treatment response was defined as a CST reduction of10 µm or more.
Results: Based on delta CST from two OCT scans, 57 eyes were responders and 16 eyes were non-responders to the initial anti-VEGF injection. Analyzing only the baseline OCT scan for each eye, the trained CNN demonstrated an area under the curve (AUC) of 0.81. At the reported operating point, the CNN correctly identified 45 of the 57 responder eyes (i.e., recall of 78.9%) and 11 of the 16 non-responder eyes (i.e., specificity of 68.8%).
Conclusions: The results of this study demonstrate the potential of a CNN to predict the response of treatment-naïve DME to a single injection of anti-VEGF therapy.
期刊介绍:
The American Journal of Ophthalmology is a peer-reviewed, scientific publication that welcomes the submission of original, previously unpublished manuscripts directed to ophthalmologists and visual science specialists describing clinical investigations, clinical observations, and clinically relevant laboratory investigations. Published monthly since 1884, the full text of the American Journal of Ophthalmology and supplementary material are also presented online at www.AJO.com and on ScienceDirect.
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