Department of Ophthalmology, Rigshospitalet, Glostrup, University of Copenhagen, Copenhagen, Denmark.
Department of Clinical Medicine. University of Copenhagen, Copenhagen, Denmark.
Transl Vis Sci Technol. 2022 Apr 1;11(4):7. doi: 10.1167/tvst.11.4.7.
The purpose of this study was to develop an automated artificial intelligence (AI) based method to quantify inflammation in the anterior chamber (AC) using anterior-segment optical coherence tomography (AS-OCT) and to explore the correlation between AI assisted AS-OCT based inflammation analyses and clinical grading of anterior uveitis by Standardization of Uveitis Nomenclature (SUN).
A prospective double blinded study of AS-OCT images of 32 eyes of 19 patients acquired by Tomey CASIA-II. OCT images were analyzed with proprietary AI-based software. Anatomic boundaries of the AC were segmented automatically by the AI software and Spearman's rank correlation between parameters related to AC cellular inflammation were calculated.
No significant (p = 0.6602) differences were found between the analyzed AC areas between samples of the different SUN grading, suggesting accurate and unbiased border detection/AC segmentation. Segmented AC areas were processed by the AI software and particles within the borders of AC were automatically counted by the software. Statistical analysis found significant (p < 0.001) correlation between clinical SUN grading and AI software detected particle count (Spearman ρ = 0.7077) and particle density (Spearman ρ = 0.7035). Significant (p < 0.001) correlation (Pearson's r = 0.9948) between manually and AI detected particles was found. No significant (p = 0.8080) difference was found between the sizes of the AI detected particles for all studies.
AI-based image analysis of AS-OCT slides show significant and independent correlation with clinical SUN assessment.
Automated AI-based AS-OCT image analysis suggests a noninvasive and quantitative assessment of AC inflammation with clear potential application in early detection and management of anterior uveitis.
本研究旨在开发一种基于人工智能（AI）的自动方法，利用眼前节光学相干断层扫描（AS-OCT）定量评估前房（AC）炎症，并探讨基于 AI 辅助 AS-OCT 的炎症分析与使用标准葡萄膜炎命名法（SUN）进行的前葡萄膜炎临床分级之间的相关性。
前瞻性、双盲研究，使用 Tomey CASIA-II 采集 19 名患者 32 只眼的 AS-OCT 图像。使用专有的基于 AI 的软件对 OCT 图像进行分析。AI 软件自动对 AC 的解剖边界进行分割，并计算与 AC 细胞炎症相关参数之间的 Spearman 秩相关系数。
在不同 SUN 分级的样本中，分析的 AC 区域之间没有发现统计学上显著的差异（p = 0.6602），这表明边界检测/AC 分割准确且无偏倚。处理分割后的 AC 区域，AI 软件自动计算边界内的颗粒数。统计分析发现，临床 SUN 分级与 AI 软件检测到的颗粒计数（Spearman ρ = 0.7077）和颗粒密度（Spearman ρ = 0.7035）之间存在显著相关性（p < 0.001）。手动和 AI 检测到的颗粒之间存在显著相关性（p < 0.001，Pearson r = 0.9948）。在所有研究中，AI 检测到的颗粒大小之间没有发现统计学上显著的差异（p = 0.8080）。
基于 AI 的 AS-OCT 幻灯片图像分析与临床 SUN 评估具有显著且独立的相关性。
李巍
