区分与偏头痛相关的视网膜微血管和神经元差异：基于深度学习的横断面研究
Discriminating retinal microvascular and neuronal differences related to migraines: Deep Learning based Crossectional Study
AI总结：
本研究利用卷积神经网络分析彩色眼底摄影和光学相干断层扫描数据，发现偏头痛患者与非偏头痛人群的视网膜微血管存在显著差异，1型CFP数据区分性能AUC最高达0.87，为偏头痛的视网膜生物标志物研究提供了新依据。
摘要：
偏头痛是一种常见的神经系统疾病，与多种提示神经元和微血管缺陷的眼部表现相关。然而，人们对视网膜成像在多大程度上可区分偏头痛患者与非偏头痛人群的了解仍然有限。本研究将卷积神经网络应用于彩色眼底摄影（CFP）和光学相干断层扫描（OCT）数据，以探究传统人工解读视网膜成像时可能无法发现的视网膜差异。研究分析了分别来自369名和336名参与者的1型[后极部]CFP和2型[视神经乳头（ONH）]CFP的回顾性数据。所有参与者的双侧视神经和黄斑均正常，无视网膜相关疾病。研究将CFP图像与OCT默认ONH测量值拼接后，输入VGG-16、ResNet-50和Inceptionv3三种卷积神经网络。主要结局指标是利用视网膜微血管和神经元成像特征区分偏头痛患者与非偏头痛人群的性能。使用1型CFP数据时，区分性能（AUC[95%CI]）较高，在0.84[0.8,0.88]至0.87[0.84,0.91]之间，且VGG-16、ResNet-50和Inceptionv3之间无显著差异。使用2型[ONH]CFP数据时，区分性能有所下降，范围从较差到一般，在0.69[0.62,0.77]至0.74[0.67,0.81]之间。总体而言，OCT默认ONH测量值对模型性能无显著贡献。类激活图（CAMs）显示血管旁弓状区是感兴趣区域。研究结果表明，与非偏头痛人群相比，偏头痛患者的视网膜微血管差异比神经元差异更为显著。
英文摘要：
Migraine, a prevalent neurological disorder, has been associated with various ocular manifestations suggestive of neuronal and microvascular deficits. However, there is limited understanding of the extent to which retinal imaging may discriminate between individuals with migraines versus without migraines. In this study, we apply convolutional neural networks to color fundus photography (CFP) and optical coherence tomography (OCT) data to investigate differences in the retina that may not be apparent through traditional human-based interpretations of retinal imaging. Retrospective data of CFP type 1 [posterior pole] and type 2 [optic nerve head (ONH)] from 369 and 336 participants respectively were analyzed. All participants had bilaterally normal optic nerves and maculae, with no retinal-involving diseases. CFP images were concatenated with OCT default ONH measurements, then inputted through three convolutional neural networks - VGG-16, ResNet-50, and Inceptionv3. The primary outcome was performance of discriminating between with migraines versus without migraines, using retinal microvascular and neuronal imaging characteristics. Using CFP type 1 data, discrimination (AUC [95% CI]) was high (0.84 [0.8, 0.88] to 0.87 [0.84, 0.91]) and not significantly different between VGG-16, ResNet-50, and Inceptionv3. Using CFP type 2 [ONH] data, discrimination was reduced and ranged from poor to fair (0.69 [0.62, 0.77] to 0.74 [0.67, 0.81]). OCT default ONH measurements overall did not significantly contribute to model performance. Class activation maps (CAMs) highlighted that the paravascular arcades were regions of interest. The findings suggest that individuals with migraines demonstrate microvascular differences more so than neuronal differences in comparison to individuals without migraines.
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