Cole Eye Institute, Cleveland Clinic, Cleveland, OH, USA.
Eye Institute, Cleveland Clinic Abu Dhabi (CCAD), Abu Dhabi, UAE.
Sci Rep. 2022 Mar 30;12(1):5392. doi: 10.1038/s41598-022-09338-y.
To develop and evaluate a fully automated pipeline that analyzes color fundus images in patients with tubercular serpiginous-like choroiditis (TB SLC) for prediction of paradoxical worsening (PW). In this retrospective study, patients with TB SLC with a follow-up of 9 months after initiation of anti-tubercular therapy were included. A fully automated custom-designed pipeline was developed which was initially tested using 12 baseline color fundus photographs for assessment of repeatability. After confirming reliability using Bland-Altman plots and intraclass correlation coefficient (ICC), the pipeline was deployed for all patients. The images were preprocessed to exclude the optic nerve from the fundus photo using a single-shot trainable WEKA segmentation algorithm. Two automatic thresholding algorithms were applied, and quantitative metrics were generated. These metrics were compared between PW + and PW- groups using non-parametric tests. A logistic regression model was used to predict probability of PW for assessing binary classification performance and receiver operator curves were generated to choose a sensitivity-optimized threshold. The study included 139 patients (139 eyes; 92 males and 47 females; mean age: 44.8 ± 11.3 years) with TB SLC. Pilot analysis of 12 images showed an excellent ICC for measuring the mean area, intensity, and integrated pixel intensity (all ICC > 0.89). The PW + group had significantly higher mean lesion area (p = 0.0152), mean pixel intensity (p = 0.0181), and integrated pixel intensity (p < 0.0001) compared to the PW- group. Using a sensitivity optimized threshold cut-off for mean pixel intensity, an area under the curve of 0.87 was achieved (sensitivity: 96.80% and specificity: 72.09%). Automated calculation of lesion metrics such as mean pixel intensity and segmented area in TB SLC is a novel approach with good repeatability in predicting PW during the follow-up.
开发并评估了一种全自动分析结核性匐行脉络膜病变（TB SLC）患者眼底彩色图像的流水线，以预测反常恶化（PW）。本回顾性研究纳入了抗结核治疗后随访 9 个月的 TB SLC 患者。开发了一种全自动定制流水线，首先使用 12 张基线眼底彩色照片进行评估，以验证其重复性。使用 Bland-Altman 图和组内相关系数（ICC）确认可靠性后，将该流水线应用于所有患者。使用单-shot 可训练 WEKA 分割算法从眼底照片中排除视神经，对图像进行预处理。应用两种自动阈值算法，并生成定量指标。使用非参数检验比较 PW+和 PW-组之间的指标。使用逻辑回归模型预测 PW 发生的概率，以评估二分类性能，并生成接收者操作特征曲线选择敏感性优化阈值。本研究纳入了 139 名 TB SLC 患者（139 只眼；92 名男性和 47 名女性；平均年龄：44.8±11.3 岁）。对 12 张图像的初步分析显示，测量平均面积、强度和积分像素强度的 ICC 均非常好（均 ICC>0.89）。PW+组的平均病变面积（p=0.0152）、平均像素强度（p=0.0181）和积分像素强度（p<0.0001）显著高于 PW-组。使用平均像素强度的优化敏感性阈值，曲线下面积为 0.87（敏感性：96.80%，特异性：72.09%）。在 TB SLC 中，自动计算病变指标（如平均像素强度和分割面积）是一种新方法，可用于预测随访期间的 PW，且重复性良好。
