白内障分级系统中的人工智能:基于LOCS iii的混合模型实现高精度分类。

IF 4.6 2区 生物学 Q2 CELL BIOLOGY
Frontiers in Cell and Developmental Biology Pub Date : 2025-09-09 eCollection Date: 2025-01-01 DOI:10.3389/fcell.2025.1669696
Gege Tang, Jie Zhang, Yingqi Du, Dexun Jiang, Yanhua Qi, Nan Zhou
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引用次数: 0

摘要

目的:设计一种基于Lens opacity Classification System III (LOCS III)的人工智能(AI)算法,实现白内障的自动诊断和分类。方法:本回顾性研究开发了一种基于人工智能的神经网络来诊断白内障和分级晶状体混浊。根据LOCS III,白内障分为核乳白色(NO)、核色(NC)、皮质(C)和后包膜下(P)。新开发的神经网络系统采用灰度化、二值化、聚类分析、“膨胀-腐蚀”等方法对图像进行处理和分析,然后研究需要测试和评估系统的泛化能力。结果:该神经网络系统能100%识别晶状体解剖结构。对核性白内障、皮质性白内障和后囊下白内障的诊断准确率为92.28% ~ 100%。该系统对白内障NO、NC、C、P的分类准确率在90.88% ~ 100%之间,曲线下面积(AUC)在96.68% ~ 100%之间。结论:基于人工智能识别算法开发一种新型的白内障诊断分级系统,建立了一套白内障自动诊断分级方案。该系统有助于快速准确的白内障诊断和分级。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Artificial intelligence in cataract grading system: a LOCS III-based hybrid model achieving high-precision classification.

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.

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来源期刊
Frontiers in Cell and Developmental Biology
Frontiers in Cell and Developmental Biology Biochemistry, Genetics and Molecular Biology-Cell Biology
CiteScore
9.70
自引率
3.60%
发文量
2531
审稿时长
12 weeks
期刊介绍: Frontiers in Cell and Developmental Biology is a broad-scope, interdisciplinary open-access journal, focusing on the fundamental processes of life, led by Prof Amanda Fisher and supported by a geographically diverse, high-quality editorial board. The journal welcomes submissions on a wide spectrum of cell and developmental biology, covering intracellular and extracellular dynamics, with sections focusing on signaling, adhesion, migration, cell death and survival and membrane trafficking. Additionally, the journal offers sections dedicated to the cutting edge of fundamental and translational research in molecular medicine and stem cell biology. With a collaborative, rigorous and transparent peer-review, the journal produces the highest scientific quality in both fundamental and applied research, and advanced article level metrics measure the real-time impact and influence of each publication.
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