AI sees beyond humans: automated diagnosis of myopia based on peripheral refraction map using interpretable deep learning

• Published in: Journal of big data Vol. 11; no. 1; pp. 125 - 11
• Main Authors: Tang, Yong, Lin, Zhenghua, Zhou, Linjing, Wang, Weijia, Wen, Longbo, Zhou, Yongli, Ge, Zongyuan, Chen, Zhao, Dai, Weiwei, Yang, Zhikuan, Tang, He, Lan, Weizhong
• Format: Journal Article
• Language: English
• Published: Cham Springer International Publishing 01.12.2024; Springer Nature B.V; SpringerOpen
• Subjects: Algorithms; Artificial intelligence; Big Data; Clinical medicine; Communications Engineering; Computational Science and Engineering; Computer Science; Data Mining and Knowledge Discovery; Database Management; Deep learning; Information Storage and Retrieval; Interpretable artificial intelligence; Machine learning; Mathematical Applications in Computer Science; Myopia; Networks; Refraction; Retina; SqueezeNet; Voptica peripheral refraction; Deep learning; SqueezeNet; Interpretable artificial intelligence; Voptica peripheral refraction; Myopia
• ISSN: 2196-1115; 2196-1115
• DOI: 10.1186/s40537-024-00989-4

The question of whether artificial intelligence (AI) can surpass human capabilities is crucial in the application of AI in clinical medicine. To explore this, an interpretable deep learning (DL) model was developed to assess myopia status using retinal refraction maps obtained with a novel peripheral refractor. The DL model demonstrated promising performance, achieving an AUC of 0.9074 (95% CI 0.83–0.97), an accuracy of 0.8140 (95% CI 0.70–0.93), a sensitivity of 0.7500 (95% CI 0.51–0.90), and a specificity of 0.8519 (95% CI 0.68–0.94). Grad-CAM analysis provided interpretable visualization of the attention of DL model and revealed that the DL model utilized information from the central retina, similar to human readers. Additionally, the model considered information from vertical regions across the central retina, which human readers had overlooked. This finding suggests that AI can indeed surpass human capabilities, bolstering our confidence in the use of AI in clinical practice, especially in new scenarios where prior human knowledge is limited. Highlights • Developing a deep learning algorithm to assess myopia status using retinal refraction maps obtained from a novel peripheral refractor. • The deep learning algorithm accurately determined myopia status with high accuracy. • Interpretable Grad-CAM analysis indicated that the deep learning algorithm utilized information from the central retina as well as vertical regions.