HRDC challenge: a public benchmark for hypertension and hypertensive retinopathy classification from fundus images

• Published in: The Visual computer Vol. 41; no. 2; pp. 1061 - 1077
• Main Authors: Qian, Bo, Wang, Xiangning, Guan, Zhouyu, Yang, Dawei, Ran, Anran, Li, Tingyao, Wang, Zheyuan, Wen, Yang, Shu, Xinming, Xie, Jinyang, Liu, Shichang, Xing, Guanyu, Silva-Rodríguez, Julio, Kobbi, Riadh, Li, Ping, Chen, Tingli, Bi, Lei, Kim, Jinman, Jia, Weiping, Li, Huating, Qin, Jing, Zhang, Ping, Cheng, Ching-Yu, Heng, Pheng-Ann, Wong, Tien Yin, Cheung, Carol Y., Tham, Yih-Chung, Thalmann, Nadia Magnenat, Sheng, Bin
• Format: Journal Article
• Language: English
• Published: Heidelberg Springer Nature B.V 01.01.2025
• Subjects: Accuracy; Algorithms; Artificial intelligence; Automation; Benchmarks; Blood pressure; Classification; Computer graphics; Datasets; Deep learning; Diagnostic systems; Hypertension; Localization; Machine learning; Medical imaging; Medical personnel; Visual impairment
• ISSN: 0178-2789; 1432-2315
• DOI: 10.1007/s00371-024-03384-5

Hypertensive retinopathy (HR) can potentially lead to vision loss if left untreated. Early screening and treatment are critical in reducing the risk of vision loss. The computer-aided diagnostic system presents an opportunity to improve the efficiency and reliability of HR screening and diagnosis, particularly given the shortage of specialized medical professionals and the challenges faced by primary care physicians in making precise diagnoses. A notable barrier to the development of such diagnostic algorithms is the lack of publicly available benchmarks and datasets. To address these issues, we organized a challenge named “HRDC—Hypertensive Retinopathy Diagnosis Challenge” in conjunction with the Computer Graphics International (CGI) 2023 conference. The challenge provided a fundus image dataset for two clinical tasks: hypertension classification and HR classification, with each task containing 1000 images. This paper presents a concise summary and analysis of the submitted methods and results for the two challenge tasks. For hypertension classification, the best performing algorithm achieved a Kappa score of 0.3819, an F1 score of 0.6337, and a specificity of 0.8472. For HR classification, the best performing algorithm achieved a Kappa score of 0.4154, an F1 score of 0.6122, and a specificity of 0.8444. We also explored an ensemble approach to the top-ranking methods, which further improved performance beyond the individual best performing algorithm for each task. The challenge results show that there is room for further optimization of these methods, but the insights and methodologies derived from this challenge provide valuable directions for developing more precise and reliable classification models for hypertension and HR.