AIROGS:RObust青光眼筛查挑战的人工智能

IF 8.9 1区 医学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS IEEE Transactions on Medical Imaging Pub Date : 2023-02-03 DOI:10.48550/arXiv.2302.01738
Coen de Vente, Koen A. Vermeer, Nicolas Jaccard, He Wang, Hongyi Sun, F. Khader, D. Truhn, Temirgali Aimyshev, Yerkebulan Zhanibekuly, Tien-Dung Le, A. Galdran, M. Ballester, G. Carneiro, G. DevikaR, S. HrishikeshP., Densen Puthussery, Hong Liu, Zekang Yang, Satoshi Kondo, S. Kasai, E. Wang, Ashritha Durvasula, J'onathan Heras, M. Zapata, Teresa Ara'ujo, Guilherme Aresta, Hrvoje Bogunovi'c, Mustafa Arikan, Y. Lee, Hyun Bin Cho, Y. Choi, Abdul Qayyum, Imran Razzak, B. Ginneken, H. Lemij, Clara I. S'anchez
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引用次数: 7

摘要

青光眼的早期发现对预防视力损害至关重要。人工智能(AI)可以以经济有效的方式分析彩色眼底照片(CFPs),使青光眼筛查更容易实现。虽然用于CFPs青光眼筛查的人工智能模型在实验室环境中显示出了有希望的结果,但由于存在分布外和低质量的图像,它们在现实场景中的性能显著下降。为了解决这个问题,我们提出了人工智能稳健青光眼筛查(AIROGS)挑战。这项挑战包括来自约6万名患者和500个不同筛查中心的约11.3万张图像的大型数据集,并鼓励开发对不可分级和意外输入数据具有鲁棒性的算法。我们在本文中评估了14个团队的解决方案,发现最好的团队的表现与一组20名专家眼科医生和验光师相似。得分最高的团队在实时检测不可分级图像时,接收器工作特征曲线下的面积为0.99 (95% CI: 0.98-0.99)。此外,在其他三个公开可用的数据集上测试时,许多算法显示出强大的性能。这些结果证明了强大的人工智能青光眼筛查的可行性。
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AIROGS: Artificial Intelligence for RObust Glaucoma Screening Challenge
The early detection of glaucoma is essential in preventing visual impairment. Artificial intelligence (AI) can be used to analyze color fundus photographs (CFPs) in a cost-effective manner, making glaucoma screening more accessible. While AI models for glaucoma screening from CFPs have shown promising results in laboratory settings, their performance decreases significantly in real-world scenarios due to the presence of out-of-distribution and low-quality images. To address this issue, we propose the Artificial Intelligence for Robust Glaucoma Screening (AIROGS) challenge. This challenge includes a large dataset of around 113,000 images from about 60,000 patients and 500 different screening centers, and encourages the development of algorithms that are robust to ungradable and unexpected input data. We evaluated solutions from 14 teams in this paper and found that the best teams performed similarly to a set of 20 expert ophthalmologists and optometrists. The highest-scoring team achieved an area under the receiver operating characteristic curve of 0.99 (95% CI: 0.98-0.99) for detecting ungradable images on-the-fly. Additionally, many of the algorithms showed robust performance when tested on three other publicly available datasets. These results demonstrate the feasibility of robust AI-enabled glaucoma screening.
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来源期刊
IEEE Transactions on Medical Imaging
IEEE Transactions on Medical Imaging 医学-成像科学与照相技术
CiteScore
21.80
自引率
5.70%
发文量
637
审稿时长
5.6 months
期刊介绍: The IEEE Transactions on Medical Imaging (T-MI) is a journal that welcomes the submission of manuscripts focusing on various aspects of medical imaging. The journal encourages the exploration of body structure, morphology, and function through different imaging techniques, including ultrasound, X-rays, magnetic resonance, radionuclides, microwaves, and optical methods. It also promotes contributions related to cell and molecular imaging, as well as all forms of microscopy. T-MI publishes original research papers that cover a wide range of topics, including but not limited to novel acquisition techniques, medical image processing and analysis, visualization and performance, pattern recognition, machine learning, and other related methods. The journal particularly encourages highly technical studies that offer new perspectives. By emphasizing the unification of medicine, biology, and imaging, T-MI seeks to bridge the gap between instrumentation, hardware, software, mathematics, physics, biology, and medicine by introducing new analysis methods. While the journal welcomes strong application papers that describe novel methods, it directs papers that focus solely on important applications using medically adopted or well-established methods without significant innovation in methodology to other journals. T-MI is indexed in Pubmed® and Medline®, which are products of the United States National Library of Medicine.
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