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
{"title":"AIROGS:RObust青光眼筛查挑战的人工智能","authors":"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","doi":"10.48550/arXiv.2302.01738","DOIUrl":null,"url":null,"abstract":"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.","PeriodicalId":13418,"journal":{"name":"IEEE Transactions on Medical Imaging","volume":" ","pages":""},"PeriodicalIF":8.9000,"publicationDate":"2023-02-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"7","resultStr":"{\"title\":\"AIROGS: Artificial Intelligence for RObust Glaucoma Screening Challenge\",\"authors\":\"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\",\"doi\":\"10.48550/arXiv.2302.01738\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"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. 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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.
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.