Goal: The purpose of this study was to identify clinically relevant patterns of glaucomatous vision loss through convex representation to predict glaucoma several years prior to disease onset. Methods: We developed a deep archetypal analysis to identify patterns of glaucomatous vision loss, and then projected visual fields over the identified patterns. Projections provided a representation that was more accurate in detecting glaucomatous vision loss, thus, more appropriate for recognizing preclinical signs of glaucoma prior to disease development. To overcome the class imbalance in prediction, we implemented a class-balanced bagging with neural networks. Results: Using original visual field as features of the class-balanced bagging classification provided an area under the receiver-operating characteristic curve (AUC) of 0.55 for predicting glaucoma approximately four years prior to disease development. Using convex representation of the visual fields as input features provided an AUC of 0.61 while using deep convex representation as input features improved the AUC to 0.71. Relevance vector machine (RVM) achieved an AUC of 0.64. Conclusion: Deep archetypal analysis representation of visual functional features with balanced bagging classification could serve as an automated tool for predicting glaucoma. Significance: Glaucoma is the second leading cause of worldwide blindness. Most people with glaucoma have no early symptoms or pain, delaying diagnosis in many patients until they reach late irreversible vision loss stages. In fact, about 50% of people with glaucoma are unaware they have the disease. Deep archetypal analysis models may impact clinical practice in effectively identifying at-risk glaucoma patients well prior to disease development.

中文翻译：

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使用深度原型分析的凸表示来预测青光眼


目标：本研究的目的是通过凸表示来识别青光眼视力丧失的临床相关模式，以在疾病发作前几年预测青光眼。方法：我们进行了深入的原型分析来识别青光眼性视力丧失的模式，然后将视野投射到识别的模式上。预测提供了在检测青光眼视力丧失方面更准确的表示，因此更适合在疾病发展之前识别青光眼的临床前体征。为了克服预测中的类别不平衡，我们使用神经网络实现了类别平衡装袋。结果：使用原始视野作为类平衡装袋分类的特征，提供了 0.55 的接受者操作特征曲线 (AUC) 下面积，用于在疾病发展前大约四年预测青光眼。使用视野的凸表示作为输入特征提供了 0.61 的 AUC，而使用深凸表示作为输入特征将 AUC 提高到 0.71。相关向量机 (RVM) 的 AUC 为 0.64。结论：具有平衡装袋分类的视觉功能特征的深度原型分析表示可以作为预测青光眼的自动化工具。意义：青光眼是全球第二大失明原因。大多数青光眼患者没有早期症状或疼痛，这导致许多患者延迟诊断，直至达到不可逆转的视力丧失晚期阶段。事实上，大约 50% 的青光眼患者并不知道自己患有这种疾病。深度原型分析模型可能会影响临床实践，以便在疾病发生之前有效识别高危青光眼患者。