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Fully automated disease severity assessment and treatment monitoring in retinopathy of prematurity using deep learning

机译：使用深度学习对早产儿视网膜病变进行全自动疾病严重性评估和治疗监控

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Retinopathy of prematurity (ROP) is a disease that affects premature infants, where abnormal growth of the retinal blood vessels can lead to blindness unless treated accordingly. Infants considered at risk of severe ROP are monitored for symptoms of plus disease, characterized by arterial tortuosity and venous dilation at the posterior pole, with a standard photographic, definition. Disagreement among ROP experts in diagnosing plus disease has driven the development of computer-based methods that classify images based on hand-crafted features extracted from the vasculature. However, most of these approaches are semi-automated, which are time-consuming and subject to variability. In contrast, deep learning is a fully automated approach that has shown great promise in a wide variety of domains, including medical genetics, informatics and imaging. Convolutional neural networks (CXNs) are deep networks which learn rich representations of disease features that are highly robust to variations in acquisition and image quality. In this study, we utilized a U-Net architecture to perform vessel segmentation and then a GoogLeXet to perform disease classification. The classifier was trained on 3,000 retinal images and validated on an independent test set of patients with different observed progressions and treatments. We show that our fully automated algorithm can be used to monitor the progression of plus disease over multiple patient visits with results that are consistent with the experts' consensus diagnosis. Future work will aim to further validate the method on larger cohorts of patients to assess its applicability within the clinic as a treatment monitoring tool.
机译：早产儿视网膜病变（ROP）是一种会影响早产儿的疾病，除非进行相应的治疗，否则视网膜血管异常生长会导致失明。监测被认为有严重ROP危险的婴儿的正病症状，其特征是动脉曲折和后极静脉扩张，并带有标准的照片定义。 ROP专家在诊断疾病方面存在分歧，这推动了基于计算机的方法的发展，该方法基于从脉管系统中提取的手工特征对图像进行分类。但是，这些方法大多数都是半自动化的，既费时又易变。相比之下，深度学习是一种完全自动化的方法，已在包括医学遗传学，信息学和影像学在内的众多领域中展现了巨大的希望。卷积神经网络（CXN）是深度网络，可学习疾病特征的丰富表示形式，这些特征对于采集和图像质量的变化具有很高的鲁棒性。在这项研究中，我们利用U-Net架构进行血管分割，然后利用GoogLeXet进行疾病分类。该分类器在3,000张视网膜图像上进行了训练，并在具有不同观察到的进展和治疗方法的患者的独立测试集中进行了验证。我们证明了我们的全自动算法可用于监测多次患者就诊时正病的进展，其结果与专家的共识诊断相一致。未来的工作将旨在进一步验证该方法在更大范围的患者群体中的地位，以评估其在临床上作为治疗监测工具的适用性。

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