Gege Tang, Jie Zhang, Yingqi Du, Dexun Jiang, Yanhua Qi, Nan Zhou
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引用次数: 0
Abstract
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.
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.