AIROGS: Artificial Intelligence for Robust Glaucoma Screening Challenge

doi:10.1109/TMI.2023.3313786

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	AIROGS: Artificial Intelligence for Robust Glaucoma Screening Challenge
	de Vente, Coen 1,2,3; Vermeer, Koenraad A.4; Jaccard, Nicolas 5; Wang, He 6,7; Sun, Hongyi 8; Khader, Firas 9; Truhn, Daniel 9; Aimyshev, Temirgali 10; Zhanibekuly, Yerkebulan 10; Le, Tien-Dung 11; Galdran, Adrian 12,13; Ballester, Miguel Angel Gonzalez 12,14,24; Carneiro, Gustavo 13,15; Devika, R. G.16; Sethumadhavan, Hrishikesh Panikkasseril 17; Puthussery, Densen 17; Liu, Hong 18; Yang, Zekang 18; Kondo, Satoshi 19; Kasai, Satoshi 20; Wang, Edward 21; Durvasula, Ashritha 21; Heras, Jonathan 22; Zapata, Miguel Angel 23; Araujo, Teresa 25; Aresta, Guilherme 25; Bogunovic, Hrvoje 25; Arikan, Mustafa 26; Lee, Yeong Chan 27; Cho, Hyun Bin 28; Choi, Yoon Ho 28,29; Qayyum, Abdul 30; Razzak, Imran 31; van Ginneken, Bram 3; Lemij, Hans G.; Sanchez, Clara I.1,2
	2024
发表期刊	IEEE TRANSACTIONS ON MEDICAL IMAGING
ISSN	0278-0062
卷号	43 期号:1 页码:542-557
摘要	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.
关键词	Color fundus photography glaucoma screening out-of-distribution detection retina robustness
DOI	10.1109/TMI.2023.3313786
收录类别	SCI
语种	英语
资助项目	Eurostars
WOS研究方向	Computer Science ; Engineering ; Imaging Science & Photographic Technology ; Radiology, Nuclear Medicine & Medical Imaging
WOS类目	Computer Science, Interdisciplinary Applications ; Engineering, Biomedical ; Engineering, Electrical & Electronic ; Imaging Science & Photographic Technology ; Radiology, Nuclear Medicine & Medical Imaging
WOS记录号	WOS:001158081600018
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
引用统计
文献类型	期刊论文
条目标识符	http://119.78.100.204/handle/2XEOYT63/38815
专题	中国科学院计算技术研究所
通讯作者	de Vente, Coen
作者单位	1.Univ Amsterdam, Informat Inst, Quantitat Healthcare Anal QurAI Grp, NL-1098 XH Amsterdam, Netherlands 2.Amsterdam UMC Locatie AMC, Dept Biomed Engn & Phys, NL-1105 AZ Amsterdam, Noord Holland, Netherlands 3.Radboudumc, Dept Radiol & Nucl Med, Diagnost Image Anal Grp DIAG, NL-6500 HB Nijmegen, Gelderland, Netherlands 4.Rotterdam Eye Hosp, Rotterdam Ophthalm Inst, NL-3011 BH Rotterdam, Netherlands 5.Project Orbis Int Inc, New York, NY 10017 USA 6.Peking Union Med Coll Hosp, Beijing 100730, Peoples R China 7.Capital Med Univ, Xuanwu Hosp, Beijing 100053, Peoples R China 8.Tsinghua Univ, Dept Automat, Beijing 100190, Peoples R China 9.Univ Hosp Aachen, Dept Diagnost & Intervent Radiol, D-52074 Aachen, Germany 10.CMC Technol LLP, Z05T0B8, Nur Sultan, Kazakhstan 11.KBC, B-1080 Brussels, Belgium 12.Univ Pompeu Fabra, Dept Tecnol Informacio & Comunicac DTIC, Barcelona 08018, Spain 13.Univ Adelaide, Australian Inst Machine Learning AIML, Adelaide, SA 5000, Australia 14.ICREA, Barcelona 08010, Spain 15.Univ Surrey, Ctr Vis Speech & Signal Proc, Guildford GU2 7XH, England 16.Coll Engn Trivandrum, Thiruvananthapuram 695016, India 17.Founding Minds Software, Thiruvananthapuram 682030, India 18.Chinese Acad Sci, Inst Comp Technol, Beijing 100190, Peoples R China 19.Muroran Inst Technol, Muroran 0508585, Japan 20.Niigata Univ Hlth & Welf, Niigata 9503102, Japan 21.Univ Western Ontario, Schulich Sch Med & Dent, London, ON N6A 5C1, Canada 22.Univ La Rioja, Dept Math & Comp Sci, Logrono 26004, Spain 23.Hosp Valle De Hebron, Sant Cugat Del Valles 08195, Spain 24.UPRetina, Barcelona 08195, Spain 25.Med Univ Vienna, Dept Ophthalmol & Optometry, Christian Doppler Lab Artificial Intelligence Reti, A-1090 Vienna, Austria 26.UCL, Inst Ophthalmol, London EC1V 9EL, England 27.Samsung Med Ctr, Res Inst Future Med, Seoul 06351, South Korea 28.Sungkyunkwan Univ, Samsung Med Ctr, Samsung Adv Inst Hlth Sci & Technol SAIHST, Dept Digital Hlth, Seoul 06351, South Korea 29.Mayo Clin, Dept Artificial Intelligence & Informat, Jacksonville, FL 32224 USA 30.Kings Coll London, Dept Biomed Engn, London, England 31.Univ New South Wales, Sch Comp Sci & Engn, Sydney, NSW 3125, Australia
推荐引用方式 GB/T 7714	de Vente, Coen,Vermeer, Koenraad A.,Jaccard, Nicolas,et al. AIROGS: Artificial Intelligence for Robust Glaucoma Screening Challenge[J]. IEEE TRANSACTIONS ON MEDICAL IMAGING,2024,43(1):542-557.
APA	de Vente, Coen.,Vermeer, Koenraad A..,Jaccard, Nicolas.,Wang, He.,Sun, Hongyi.,...&Sanchez, Clara I..(2024).AIROGS: Artificial Intelligence for Robust Glaucoma Screening Challenge.IEEE TRANSACTIONS ON MEDICAL IMAGING,43(1),542-557.
MLA	de Vente, Coen,et al."AIROGS: Artificial Intelligence for Robust Glaucoma Screening Challenge".IEEE TRANSACTIONS ON MEDICAL IMAGING 43.1(2024):542-557.