The purpose of this study was to assess unique corneal tomographic parameters of allergic eye disease (AED) using optical coherence tomography (OCT) and artificial intelligence (AI). A total of 57 eyes diagnosed with AED were included. The curvature and aberrations of the air‐epithelium (A‐E) and epithelium‐Bowman's layer (E‐B) interfaces were calculated. Random forest AI models were built combing this data with the parameters of healthy, forme fruste keratoconus (FFKC) and KC eyes. The AI models were cross‐validated with 3‐fold random sampling. Each model was limited to 10 trees. The AI model incorporating both A‐E and E‐B parameters provided the best classification of AED eyes (area under the curve = 0.958, sensitivity = 80.7%, specificity = 98.5%, precision = 88.2%). Further, the E‐B interface parameters provided the highest information gain in the AI model. A few AED eyes (n = 9) had tomography parameters similar to FFKC and KC eyes and may be at risk of progression to KC.

中文翻译：

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通过OCT成像和人工智能识别的过敏性眼病的独特角膜层析成像特征。

这项研究的目的是使用光学相干断层扫描（OCT）和人工智能（AI）评估过敏性眼病（AED）的独特角膜断层扫描参数。总共包括57只被诊断出患有AED的眼睛。计算了空气上皮（A-E）和上皮-鲍曼层（E-B）界面的曲率和像差。建立了随机森林AI模型，将这些数据与健康的圆锥形圆锥角膜（FFKC）和KC眼睛的参数结合在一起。AI模型通过3倍随机抽样进行交叉验证。每个模型仅限于10棵树。结合A‐E和E‐B参数的AI模型提供了AED眼睛的最佳分类（曲线下的面积= 0.958，灵敏度= 80.7％，特异性= 98.5％，精度= 88.2％）。进一步，E-B接口参数在AI模型中提供了最高的信息增益。几只AED眼睛（n = 9）的断层扫描参数类似于FFKC和KC眼睛，可能有发展为KC的风险。