用于高分辨率眼表摄影的结膜球红提取管道。

IF 2.6 3区 医学 Q2 OPHTHALMOLOGY Translational Vision Science & Technology Pub Date : 2025-01-02 DOI:10.1167/tvst.14.1.6
Philipp Ostheimer, Arno Lins, Lars Albert Helle, Vito Romano, Bernhard Steger, Marco Augustin, Daniel Baumgarten
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引用次数: 0

摘要

目的:通过实施图像分析管道,从一种新型成像系统的标准化高分辨率眼表照片中提取结膜球红肿。方法:数据来自两项试验(健康;即将离任的眼科诊所)收集、加工和训练机器学习模型用于眼部表面分割。定义了各种感兴趣的区域,以全局和局部提取基于颜色强度的红色生物标志物。基于图像的发红评分与临床评分(Efron)相关,以进行验证。结果:验证了确定感兴趣区域的模型的分割性能,得到了0.9639(虹膜)和0.9731(眼表)的平均相交。对所有试验数据进行分析,并建立了新型成像系统的数字分级量表。隔几周就诊的照片和红肿评分显示了良好的可行性和可重复性。对于同一时段内的得分,平均变异系数为4.09%。临床分级与中度Spearman正相关(0.599)。结论:本文提出的结膜球红度提取管道表明,通过标准化成像,可以对分割模型和基于图像的外眼摄影红度评分进行分类和评价。因此,它显示了为眼科保健专业人员提供一种客观的工具来分级眼红肿,并以高通量的方式促进临床决策的潜力。翻译相关性:通过标准化成像与基于人工智能的分析工具相结合,为临床医生和研究人员提供高通量工作流程,客观地确定基于图像的红肿评分。
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Conjunctival Bulbar Redness Extraction Pipeline for High-Resolution Ocular Surface Photography.

Purpose: To extract conjunctival bulbar redness from standardized high-resolution ocular surface photographs of a novel imaging system by implementing an image analysis pipeline.

Methods: Data from two trials (healthy; outgoing ophthalmic clinic) were collected, processed, and used to train a machine learning model for ocular surface segmentation. Various regions of interest were defined to globally and locally extract a redness biomarker based on color intensity. The image-based redness scores were correlated to clinical gradings (Efron) for validation.

Results: The model to determine the regions of interest was verified for a segmentation performance, yielding mean intersections over union of 0.9639 (iris) and 0.9731 (ocular surface). All trial data were analyzed and a digital grading scale for the novel imaging system was established. Photographs and redness scores from visits weeks apart showed good feasibility and reproducibility. For scores within the same session, a mean coefficient of variation of 4.09% was observed. A moderate positive Spearman correlation (0.599) was found with clinical grading.

Conclusions: The proposed conjunctival bulbar redness extraction pipeline demonstrates that by using standardized imaging, a segmentation model and image-based redness scores' external eye photography can be classified and evaluated. Therefore, it shows the potential to provide eye care professionals with an objective tool to grade ocular redness and facilitate clinical decision-making in a high-throughput manner.

Translational relevance: To empower clinicians and researchers with a high-throughput workflow by standardized imaging combined with an analysis tool based on artificial intelligence to objectively determine an image-based redness score.

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来源期刊
Translational Vision Science & Technology
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.
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