Frontiers in cell and developmental biology · 2025
Artificial intelligence in cataract grading system: a LOCS III-based hybrid model achieving high-precision classification.
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Tang Gege, Zhang Jie, Du Yingqi, Jiang Dexun, Qi Yanhua, Zhou Nan
Аннотация
PURPOSE: To 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.
METHODS: This 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.
RESULTS: The 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%.
CONCLUSION: A 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.
METHODS: This 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.
RESULTS: The 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%.
CONCLUSION: A 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.