Ophthalmic Segmentation and Analysis Software (OASIS): A Comprehensive Tool for Quantitative Evaluation of Meibography Images.

IF 2.6 3区医学 Q2 OPHTHALMOLOGY

Translational Vision Science & Technology Pub Date : 2025-09-02 DOI:10.1167/tvst.14.9.22

Naomi Joseph, Ved Shivade, Jiawei Chen, Ian Marshall, Emma Avery, Dominique Jennings, Harry Menegay, Ramkumar Ramamirtham, David Wilson, Beth Ann Benetz, Thomas Stokkermans

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引用次数: 0

Abstract

Purpose: To develop an interactive image editor, Ophthalmic Segmentation and Analysis Software (OASIS) for automating and improving the analysis of meibography images to gain insight into the progression of meibomian gland dysfunction.

Methods: A natural history study was conducted, collecting 2,439 meibography images from 325 patients. Clinicians used OASIS for image analysis, which involved both manual and deep-learning assisted processes. In the manual process, clinicians annotated three distinct masks per image: the eyelid, glands, and gland loss. In the assisted process, OASIS incorporated deep-learning models to infer gland masks, reducing the time required for gland-by-gland annotation. The software's interface provided additional tools for image enhancement and calculation of currently used clinical metrics including the Pult scale.

Results: OASIS enabled clinicians to quantitatively analyze MGD in under 3 minutes, representing an 87% reduction in time compared to traditional manual analysis methods. The software accurately calculated Pult meiboscale grades with fair agreement between the clinician and software (kappa = 0.79), demonstrating a high level of consistency.

Conclusions: OASIS significantly streamlines the analysis of meibography images and allows for a more objective and efficient evaluation of MGD. By implementing deep learning models for gland inference and providing a suite of custom annotation tools, OASIS may reduce the time burden on clinicians while maintaining accuracy.

Translational relevance: OASIS paves the way for developing quantitative biomarkers for MGD and may have applications in both clinical practices and research. OASIS also further demonstrates the potential for AI-driven tools in improving ophthalmic image analysis.

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眼科分割和分析软件（OASIS）：一种用于定量评价Meibography图像的综合工具。

目的：开发一种交互式图像编辑器，眼科分割和分析软件（OASIS），用于自动化和改进睑板造影图像的分析，以深入了解睑板腺功能障碍的进展。方法：采用自然历史研究方法，收集325例患者的2439张meibography图像。临床医生使用OASIS进行图像分析，其中包括人工和深度学习辅助过程。在手工过程中，临床医生在每张图像上标注了三个不同的掩膜：眼睑、腺体和腺体丢失。在辅助过程中，OASIS结合了深度学习模型来推断腺体掩模，减少了逐个腺体注释所需的时间。该软件的界面为图像增强和计算当前使用的临床指标（包括普尔量表）提供了额外的工具。结果：OASIS使临床医生能够在3分钟内定量分析MGD，与传统的人工分析方法相比，减少了87%的时间。该软件准确地计算出了临床医生和软件之间相当一致的Pult meiboscale评分（kappa = 0.79），显示出高度的一致性。结论：OASIS显着简化了meibography图像的分析，并允许对MGD进行更客观有效的评估。通过实施用于腺体推断的深度学习模型和提供一套自定义注释工具，OASIS可以在保持准确性的同时减少临床医生的时间负担。转化相关性：OASIS为开发MGD的定量生物标志物铺平了道路，可能在临床实践和研究中都有应用。OASIS还进一步展示了人工智能驱动工具在改善眼科图像分析方面的潜力。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Translational Vision Science & Technology Engineering-Biomedical Engineering

CiteScore

5.70

自引率

3.30%

发文量

346

审稿时长

25 weeks

期刊介绍： Translational Vision Science & Technology (TVST), an official journal of the Association for Research in Vision and Ophthalmology (ARVO), an international organization whose purpose is to advance research worldwide into understanding the visual system and preventing, treating and curing its disorders, is an online, open access, peer-reviewed journal emphasizing multidisciplinary research that bridges the gap between basic research and clinical care. A highly qualified and diverse group of Associate Editors and Editorial Board Members is led by Editor-in-Chief Marco Zarbin, MD, PhD, FARVO. The journal covers a broad spectrum of work, including but not limited to: Applications of stem cell technology for regenerative medicine, Development of new animal models of human diseases, Tissue bioengineering, Chemical engineering to improve virus-based gene delivery, Nanotechnology for drug delivery, Design and synthesis of artificial extracellular matrices, Development of a true microsurgical operating environment, Refining data analysis algorithms to improve in vivo imaging technology, Results of Phase 1 clinical trials, Reverse translational ("bedside to bench") research. TVST seeks manuscripts from scientists and clinicians with diverse backgrounds ranging from basic chemistry to ophthalmic surgery that will advance or change the way we understand and/or treat vision-threatening diseases. TVST encourages the use of color, multimedia, hyperlinks, program code and other digital enhancements.