A critical complication after cataract surgery is the dislocation of the lens implant leading to vision deterioration and eye trauma. In order to reduce the risk of this complication, it is vital to discover the risk factors during the surgery. However, studying the relationship between lens dislocation and its suspicious risk factors using numerous videos is a time-extensive procedure. Hence, the surgeons demand an automatic approach to enable a larger-scale and, accordingly, more reliable study. In this paper, we propose a novel framework as the major step towards lens irregularity detection. In particular, we propose (I) an end-to-end recurrent neural network to recognize the lens-implantation phase and (II) a novel semantic segmentation network to segment the lens and pupil after the implantation phase. The phase recognition results reveal the effectiveness of the proposed surgical phase recognition approach. Moreover, the segmentation results confirm the proposed segmentation network's effectiveness compared to state-of-the-art rival approaches.

翻译：白内障手术后的关键并发症是透镜植入器的脱落,导致视力恶化和眼部创伤。为了降低这一并发症的风险,在手术期间必须发现风险因素。然而,利用许多视频研究透镜脱乱与其可疑风险因素之间的关系是一个时间延伸的过程。因此,外科医生要求采用自动方法,以便能够进行更大规模、因此更可靠的研究。在本文件中,我们提议建立一个新框架,作为透镜不规则性检测的主要步骤。特别是,我们提议(一) 建立端对端经常性神经网络,以识别透镜植入阶段,(二) 建立一个新颖的语义分解网络,在植入阶段后将镜头和学生分解。阶段识别结果显示拟议的外科阶段识别方法的有效性。此外,分解结果证实了拟议的分解网络相对于最先进的对立方法的有效性。