DETI/IEETA, University of Aveiro, Portugal.
DETI/IEETA, University of Aveiro, Portugal.
Int J Med Inform. 2018 Dec;120:137-146. doi: 10.1016/j.ijmedinf.2018.10.005. Epub 2018 Oct 18.
Diabetic retinopathy (DR) is the most prevalent microvascular complication of diabetes mellitus and can lead to irreversible visual loss. Screening programs, based on retinal imaging techniques, are fundamental to detect the disease since the initial stages are asymptomatic. Most of these examinations reflect negative cases and many have poor image quality, representing an important inefficiency factor. The SCREEN-DR project aims to tackle this limitation, by researching and developing computer-aided methods for diabetic retinopathy detection. This article presents a multidisciplinary collaborative platform that was created to meet the needs of physicians and researchers, aiming at the creation of machine learning algorithms to facilitate the screening process.
Our proposal is a collaborative platform for textual and visual annotation of image datasets. The architecture and layout were optimized for annotating DR images by gathering feedback from several physicians during the design and conceptualization of the platform. It allows the aggregation and indexing of imagiology studies from diverse sources, and supports the creation and annotation of phenotype-specific datasets to feed artificial intelligence algorithms. The platform makes use of an anonymization pipeline and role-based access control for securing personal data.
The SCREEN-DR platform has been deployed in the production environment of the SCREEN-DR project at http://demo.dicoogle.com/screen-dr, and the source code of the project is publicly available. We provide a description of the platform's interface and use cases it supports. At the time of publication, four physicians have created a total of 1826 annotations for 701 distinct images, and the annotated data has been used for training classification models.
糖尿病视网膜病变（DR）是糖尿病最常见的微血管并发症，可导致不可逆转的视力丧失。基于视网膜成像技术的筛查计划对于早期无症状的疾病检测至关重要。这些检查大多反映了阴性病例，且许多图像质量较差，这是一个重要的效率低下因素。SCREEN-DR 项目旨在通过研究和开发用于糖尿病视网膜病变检测的计算机辅助方法来解决这一局限性。本文介绍了一个多学科协作平台，该平台是为满足医生和研究人员的需求而创建的，旨在创建机器学习算法以促进筛查过程。
我们的提案是一个用于图像数据集文本和视觉注释的协作平台。通过在平台的设计和概念化阶段收集多位医生的反馈，对架构和布局进行了优化，以用于 DR 图像的注释。它允许从不同来源聚合和索引影像学研究，并支持创建和注释表型特异性数据集，以为人工智能算法提供支持。该平台利用匿名化管道和基于角色的访问控制来保护个人数据。
SCREEN-DR 平台已在 SCREEN-DR 项目的生产环境中部署，网址为 http://demo.dicoogle.com/screen-dr，该项目的源代码是公开的。我们提供了平台界面的描述以及它支持的用例。截至出版时，已有四位医生共为 701 张不同的图像创建了 1826 条注释，并且已使用注释数据来训练分类模型。
