AUTHOR=Tang Gege , Zhang Jie , Du Yingqi , Jiang Dexun , Qi Yanhua , Zhou Nan 

TITLE=Artificial intelligence in cataract grading system: a LOCS III-based hybrid model achieving high-precision classification

JOURNAL=Frontiers in Cell and Developmental Biology

VOLUME=Volume 13 - 2025

YEAR=2025

URL=https://www.frontiersin.org/journals/cell-and-developmental-biology/articles/10.3389/fcell.2025.1669696

DOI=10.3389/fcell.2025.1669696

ISSN=2296-634X

ABSTRACT=PurposeTo design an artificial intelligence (AI) algorithm based on the Lens Opacities Classification System III (LOCS III) to realize automatic diagnosis of cataracts and classification of its.MethodsThis retrospective study develops an AI-based neural network to diagnose cataracts and grade lens opacity. According to the LOCS III, cataracts are classified into Nuclear Opalescence (NO), Nuclear Color (NC), Cortical(C) and Posterior subcapsular(P). The newly developed neural network system uses grayscale, binarization, cluster analysis, “dilation-corrosion” and other methods to process and analyze the images, then the study need to test and evaluate the generalization ability of the system.ResultsThe new neural network system can identify 100% of lens anatomy. It has an accuracy of 92.28%–100% in the diagnosis of nuclear cataract, cortical cataract and posterior subcapsular cataract. The classification accuracy rate of the system for cataract NO, NC, C, P is between 90.88% and 100%, the Area Under the Curve (AUC) is between 96.68% and 100%.ConclusionA novel cataract diagnostic and grading system can be developed based on the AI recognition algorithm, which establishes an automatic cataract diagnosis and grading scheme. The system facilitates rapid and accurate cataract diagnosis and grading.