Abnormalities in pupillary light reflex can indicate optic nerve disorders that may lead to permanent visual loss if not diagnosed in an early stage. In this study, we focus on relative afferent pupillary defect (RAPD), which is based on the difference between the reactions of the eyes when they are exposed to light stimuli. Incumbent RAPD assessment methods are based on subjective practices that can lead to unreliable measurements. To eliminate subjectivity and obtain reliable measurements, we introduced an automated framework to detect RAPD. For validation, we conducted a clinical study with lab-on-a-headset, which can perform automated light reflex test. In addition to benchmarking handcrafted algorithms, we proposed a transfer learning-based approach that transformed a deep learning-based generic object recognition algorithm into a pupil detector. Based on the conducted experiments, proposed algorithm RAPDNet can achieve a sensitivity and a specificity of 90.6% over 64 test cases in a balanced set, which corresponds to an AUC of 0.929 in ROC analysis. According to our benchmark with three handcrafted algorithms and nine performance metrics, RAPDNet outperforms all other algorithms in every performance category.

中文翻译：

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通过转移学习筛选相对传入瞳孔缺损

瞳孔光反射异常可能表明视神经疾病，如果不早期诊断，可能会导致永久性视力丧失。在这项研究中，我们集中在相对传入瞳孔缺损（RAPD），这是基于当眼睛受到光刺激时眼睛的反应之间的差异。现有的RAPD评估方法基于主观做法，这可能会导致测量结果不可靠。为了消除主观性并获得可靠的测量结果，我们引入了自动框架来检测RAPD。为了进行验证，我们使用耳机实验室进行了一项临床研究，该实验室可以执行自动光反射测试。除了对手工算法进行基准测试外，我们提出了一种基于转移学习的方法，该方法将基于深度学习的通用对象识别算法转换为瞳孔检测器。基于所进行的实验，提出的算法RAPDNet在平衡集中的64个测试案例上可以达到90.6％的灵敏度和特异性，在ROC分析中对应的AUC为0.929。根据我们的基准，其中包含三种手工算法和九种性能指标，RAPDNet在所有性能类别中均胜过所有其他算法。