Department of Biomedical Engineering, Duke University, Durham, NC, USA.
Department of Ophthalmology and Visual Sciences, Kellogg Eye Center, University of Michigan, Ann Arbor, MI, USA.
Transl Vis Sci Technol. 2021 Oct 4;10(12):2. doi: 10.1167/tvst.10.12.2.
To assess clinical applicability of automatic image analysis in microbial keratitis (MK) by evaluating the relationship between biomarker measurements on slit-lamp photography (SLP) and best-corrected visual acuity (BCVA).
Seventy-six patients with MK with SLP images and same-day logarithm of the minimum angle of resolution (logMAR) BCVA were evaluated. MK biomarkers (stromal infiltrate, white blood cell infiltration, corneal edema, hypopyon, epithelial defect) were segmented manually by ophthalmologists and automatically by a novel, open-source, deep learning-based segmentation algorithm. Five measurements (presence, maximum width, total area, proportion of the corneal limbus area affected, centrality) were calculated. Correlations between the measurements and BCVA were calculated. An automatic regression model estimated BCVA from the measurements. Differences in performance between using manual and automatic measurements were evaluated using William's test (for correlation) and the paired-sample t-test (for absolute error).
Measurements had high correlations of 0.86 (manual) and 0.84 (automatic) with true BCVA. Estimated BCVA had average (mean ± SD) absolute errors of 0.39 ± 0.27 logMAR (manual, median: 0.30) and 0.35 ± 0.28 logMAR (automatic, median: 0.30) and high correlations of 0.76 (manual) and 0.80 (automatic) with true BCVA. Differences between using manual and automatic measurements were not statistically significant for correlations of measurements with true BCVA (P = .66), absolute errors of estimated BCVA (P = .15), or correlations of estimated BCVA with true BCVA (P = .60).
The proposed algorithm measured MK biomarkers as accurately as ophthalmologists. Measurements were highly correlated with and estimative of visual acuity.
This study demonstrates the potential of developing fully automatic objective and standardized strategies to aid ophthalmologists in the clinical assessment of MK.
通过评估生物标志物测量值与最佳矫正视力（BCVA）之间的关系，评估自动图像分析在微生物角膜炎（MK）中的临床适用性。
对 76 例 MK 患者的 SL 图像和同天对数最小分辨角（logMAR）BCVA 进行评估。MK 生物标志物（基质浸润、白细胞浸润、角膜水肿、前房积脓、上皮缺损）由眼科医生手动和一种新的开源深度学习分割算法自动分割。计算了 5 个测量值（存在、最大宽度、总面积、角膜缘受累面积比例、中心性）。计算了测量值与 BCVA 之间的相关性。自动回归模型根据测量值估计 BCVA。使用威廉检验（相关性）和配对样本 t 检验（绝对误差）评估手动和自动测量值之间的性能差异。
测量值与真实 BCVA 的相关性分别为 0.86（手动）和 0.84（自动），具有很高的相关性。估计的 BCVA 的平均（均值±标准差）绝对误差分别为 0.39±0.27logMAR（手动，中位数：0.30）和 0.35±0.28logMAR（自动，中位数：0.30），与真实 BCVA 的相关性分别为 0.76（手动）和 0.80（自动），具有很高的相关性。手动和自动测量值与真实 BCVA 的相关性（P=0.66）、估计的 BCVA 的绝对误差（P=0.15）或估计的 BCVA 与真实 BCVA 的相关性（P=0.60）差异均无统计学意义。
所提出的算法能够像眼科医生一样准确地测量 MK 生物标志物。测量值与视力高度相关且可估计视力。
郑思慧
