Blepharoptosis, or ptosis as it is more commonly referred to, is a condition of the eyelid where the upper eyelid droops. The current diagnosis for ptosis involves cumbersome manual measurements that are time-consuming and prone to human error. In this paper, we present AutoPtosis, an artificial intelligence based system with interpretable results for rapid diagnosis of ptosis. We utilize a diverse dataset collected from the Illinois Ophthalmic Database Atlas (I-ODA) to develop a robust deep learning model for prediction and also develop a clinically inspired model that calculates the marginal reflex distance and iris ratio. AutoPtosis achieved 95.5% accuracy on physician verified data that had an equal class balance. The proposed algorithm can help in the rapid and timely diagnosis of ptosis, significantly reduce the burden on the healthcare system, and save the patients and clinics valuable resources.
翻译：Blephoproptois, 或更常见的这种病症,是眼皮的一个条件,即上眼皮下垂的眼皮。目前对ptonis的诊断涉及繁琐的人工测量,耗费时间,容易发生人为错误。本文介绍AutoPtosis,这是一个人工智能系统,具有可解释的快速诊断病症的结果。我们使用从伊利诺伊州眼科数据库Atlas(I-ODA)收集的多种数据集,开发一个强有力的深度预测学习模型,并开发一个临床启发模型,计算边际反射距离和iris比率。AutoPtosis在医生核实的数据中实现了95.5%的精确度,而医生核实的数据具有同等的等级平衡。提议的算法可以帮助快速、及时地诊断ptois,大大减轻保健系统的负担,并节省病人和诊所的宝贵资源。
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