Progress in artificial intelligence for predicting therapeutic efficacy of intravitreal injection
10.3980/j.issn.1672-5123.2026.4.23
- VernacularTitle:人工智能预测玻璃体腔内注射疗效的研究进展
- Author:
Xiaofeng WU
1
;
Jiayi ZHANG
1
;
Chunyan XIAO
1
;
Yanshuang GENG
1
;
Yonggang LIU
1
;
Boxuan SONG
1
;
Jiawei WANG
1
Author Information
1. Department of Ophthalmology, Qilu Hospital of Shandong University, Jinan 250012,Shandong Province, China
- Publication Type:Journal Article
- Keywords:
anti-VEGF therapy;
artificial intelligence;
efficacy prediction;
intravitreal injection
- From:
International Eye Science
2026;26(4):687-693
- CountryChina
- Language:Chinese
-
Abstract:
Intravitreal anti-vascular endothelial growth factor(anti-VEGF)therapy has been widely used, but the variability in its therapeutic efficacy limits individualized treatment. In recent years, the application of artificial intelligence(AI)has opened up new avenues for personalized treatment response prediction, and its core branches include machine learning(ML)and deep learning(DL). This review systematically retrieved and analyzed 41 relevant studies published up to April 2025. Comprehensive analysis reveals that AI predictive models are evolving from forecasting single endpoints(such as visual acuity or central retinal thickness)to integrating multi-dimensional endpoints(encompassing anatomical, functional, and treatment demand parameters)and generating predictive imaging outputs. In terms of technical approaches, DL models(28 studies, accounting for 68.3%)dominate this field due to their robust image interpretation capabilities, while ML models(10 studies, 24.4%)retain significant value in the analysis of structured clinical data. Cross-disease comparisons indicate that research efforts are most concentrated on age-related macular degeneration(ARMD)and diabetic macular edema(DME), with shared conceptual frameworks for model construction, yet distinct anatomical and functional indicators are prioritized for each disease. Currently, the field confronts several key challenges, including insufficient prospective clinical validation, limited model interpretability(the “black box problem”), and a scarcity of high-quality multi-center datasets. Moving forward, it is imperative to advance real-world validation and develop explainable AI techniques to expedite the clinical translation of these predictive models.
