Spectral-domain optical coherence tomography (SDOCT) is the gold standard of imaging the eye in clinics. Penetration depth with such devices is, however, limited and visualization of the choroid, which is essential for diagnosing chorioretinal disease, remains limited. Whereas swept-source OCT (SSOCT) devices allow for visualization of the choroid these instruments are expensive and availability in praxis is limited. We present an artificial intelligence (AI)-based solution to enhance the visualization of the choroid in OCT scans and allow for quantitative measurements of choroidal metrics using generative deep learning (DL). Synthetically enhanced SDOCT B-scans with improved choroidal visibility were generated, leveraging matching images to learn deep anatomical features during the training. Using a single-center tertiary eye care institution cohort comprising a total of 362 SDOCT-SSOCT paired subjects, we trained our model with 150,784 images from 410 healthy, 192 glaucoma, and 133 diabetic retinopathy eyes. An independent external test dataset of 37,376 images from 146 eyes was deployed to assess the authenticity and quality of the synthetically enhanced SDOCT images. Experts’ ability to differentiate real versus synthetic images was poor (47.5% accuracy). Measurements of choroidal thickness, area, volume, and vascularity index, from the reference SSOCT and synthetically enhanced SDOCT, showed high Pearson’s correlations of 0.97 [95% CI: 0.96–0.98], 0.97 [0.95–0.98], 0.95 [0.92–0.98], and 0.87 [0.83–0.91], with intra-class correlation values of 0.99 [0.98–0.99], 0.98 [0.98–0.99], and 0.95 [0.96–0.98], 0.93 [0.91–0.95], respectively. Thus, our DL generative model successfully generated realistic enhanced SDOCT data that is indistinguishable from SSOCT images providing improved visualization of the choroid. This technology enabled accurate measurements of choroidal metrics previously limited by the imaging depth constraints of SDOCT. The findings open new possibilities for utilizing affordable SDOCT devices in studying the choroid in both healthy and pathological conditions.

谱域光学相干断层扫描 (SDOCT) 是临床眼部成像的黄金标准。然而，此类设备的穿透深度有限，并且对于诊断脉络膜视网膜疾病至关重要的脉络膜的可视化仍然有限。虽然扫源 OCT (SSOCT) 设备可以实现脉络膜的可视化，但这些仪器价格昂贵，而且在实践中的可用性有限。我们提出了一种基于人工智能 (AI) 的解决方案，以增强 OCT 扫描中脉络膜的可视化，并允许使用生成深度学习 (DL) 定量测量脉络膜指标。生成了具有改善的脉络膜可视性的综合增强 SDOCT B 扫描，利用匹配图像在训练期间学习深层解剖特征。我们使用由 362 名 SDOCT-SSOCT 配对受试者组成的单中心三级眼保健机构队列，使用来自 410 只健康眼睛、192 只青光眼和 133 只糖尿病视网膜病变眼睛的 150,784 张图像来训练我们的模型。部署了包含来自 146 只眼睛的 37,376 幅图像的独立外部测试数据集，以评估综合增强的 SDOCT 图像的真实性和质量。专家区分真实图像和合成图像的能力很差（准确度为 47.5%）。根据参考 SSOCT 和综合增强 SDOCT 测量的脉络膜厚度、面积、体积和血管分布指数显示，Pearson 相关性高达 0.97 [95% CI：0.96–0.98]、0.97 [0.95–0.98]、0.95 [0.92–0.98] ] 和 0.87 [0.83–0.91]，组内相关值分别为 0.99 [0.98–0.99]、0.98 [0.98–0.99] 和 0.95 [0.96–0.98]、0.93 [0.91–0.95]。因此，我们的深度学习生成模型成功生成了逼真的增强型 SDOCT 数据，该数据与 SSOCT 图像无法区分，从而提供了改进的脉络膜可视化效果。该技术能够准确测量脉络膜指标，以前受到 SDOCT 成像深度限制的限制。这些发现为利用经济实惠的 SDOCT 设备研究健康和病理条件下的脉络膜开辟了新的可能性。