The efficacy of deep learning in predicting successful big-bubble (SBB) formation during deep anterior lamellar keratoplasty (DALK) was evaluated. Medical records of patients undergoing DALK at the University of Cologne, Germany between March 2013 and July 2019 were retrospectively analyzed. Patients were divided into two groups: (1) SBB or (2) failed big-bubble (FBB). Preoperative images of anterior segment optical coherence tomography and corneal biometric values (corneal thickness, corneal curvature, and densitometry) were evaluated. A deep neural network model, Visual Geometry Group-16, was selected to test the validation data, evaluate the model, create a heat map image, and calculate the area under the curve (AUC). This pilot study included 46 patients overall (11 women, 35 men). SBBs were more common in keratoconus eyes (KC eyes) than in corneal opacifications of other etiologies (non KC eyes) (p = 0.006). The AUC was 0.746 (95% confidence interval [CI] 0.603–0.889). The determination success rate was 78.3% (18/23 eyes) (95% CI 56.3–92.5%) for SBB and 69.6% (16/23 eyes) (95% CI 47.1–86.8%) for FBB. This automated system demonstrates the potential of SBB prediction in DALK. Although KC eyes had a higher SBB rate, no other specific findings were found in the corneal biometric data.

评估了深度学习在预测深前板层角膜移植术 (DALK) 期间成功形成大气泡 (SBB) 的功效。回顾性分析了 2013 年 3 月至 2019 年 7 月在德国科隆大学接受 DALK 的患者的病历。患者被分为两组：(1) SBB 或 (2) 失败的大气泡 (FBB)。评估了眼前节光学相干断层扫描和角膜生物特征值（角膜厚度、角膜曲率和密度测定）的术前图像。选择深度神经网络模型 Visual Geometry Group-16 来测试验证数据、评估模型、创建热图图像并计算曲线下面积 (AUC)。该试点研究总共包括 46 名患者（11 名女性，35 名男性）。p  = 0.006）。AUC 为 0.746（95% 置信区间 [CI] 0.603–0.889）。SBB 的测定成功率为 78.3%（18/23 眼）（95% CI 56.3-92.5%），FBB 为 69.6%（16/23 眼）（95% CI 47.1-86.8%）。该自动化系统展示了 DALK 中 SBB 预测的潜力。尽管 KC 眼的 SBB 率较高，但在角膜生物特征数据中未发现其他具体发现。