Department of General Medicine, Kaohsiung Medical University Hospital, Kaohsiung Medical University, Kaohsiung, Taiwan.
Shiley Eye Institute and Viterbi Family Department of Ophthalmology, University of California, San Diego, USA.
Sci Rep. 2021 Sep 14;11(1):18208. doi: 10.1038/s41598-021-97567-y.
This study aimed to evaluate the reliability of in vivo confocal microscopic neuroanalysis by beginners using manual and automated modules. Images of sub-basal corneal nerve plexus (SCNP) from 108 images of 18 healthy participants were analyzed by 7 beginner observers using manual (CCMetrics, [CCM]) and automated (ACCMetrics, [ACCM]) module. SCNP parameters analyzed included corneal nerve fiber density (NFD), corneal nerve branch density (NBD), corneal nerve fiber length (NFL), and tortuosity coefficient (TC). The intra-observer repeatability, inter-observer reliability, inter-module agreement, and left-right eye symmetry level of SCNP parameters were examined. All observers showed good intra-observer repeatability using CCM (intraclass correlation coefficient [ICC] > 0.60 for all), except when measuring TC. Two observers demonstrated especially excellent repeatability in analyzing NFD, NBD, and NFL using manual mode, indicating the quality of interpretation may still be observer-dependent. Among all SCNP parameters, NFL had the best inter-observer reliability (Spearman's rank-sum correlation coefficient [SpCC] and ICC > 0.85 for the 3 original observers) and left-right symmetry level (SpCC and ICC > 0.60). In the additional analysis of inter-observer reliability using results by all 7 observers, only NFL showed good inter-observer reliability (ICC = 0.79). Compared with CCM measurements, values of ACCM measurements were significantly lower, implying a poor inter-module agreement. Our result suggested that performance of quantitative corneal neuroanalysis by beginners maybe acceptable, with NFL being the most reliable parameter, and automated method cannot fully replace manual work.
本研究旨在评估初学者使用手动和自动模块进行活体共聚焦显微镜神经分析的可靠性。18 名健康参与者的 108 张图像的基底下角膜神经丛(SCNP)图像由 7 名初学者观察者使用手动(CCMetrics,[CCM])和自动(ACCMetrics,[ACCM])模块进行分析。分析的 SCNP 参数包括角膜神经纤维密度(NFD)、角膜神经分支密度(NBD)、角膜神经纤维长度(NFL)和扭曲系数(TC)。检查了 SCNP 参数的观察者内重复性、观察者间可靠性、模块间一致性和左右眼对称性水平。所有观察者使用 CCM 均显示出良好的观察者内重复性(所有 ICC > 0.60),除了测量 TC 时。两名观察者在使用手动模式分析 NFD、NBD 和 NFL 时表现出特别出色的重复性,这表明解释的质量可能仍然依赖于观察者。在所有 SCNP 参数中,NFL 具有最佳的观察者间可靠性(原始 3 名观察者的 Spearman 秩和相关系数[SpCC]和 ICC > 0.85)和左右对称性水平(SpCC 和 ICC > 0.60)。在使用所有 7 名观察者的结果进行观察者间可靠性的额外分析中,只有 NFL 显示出良好的观察者间可靠性(ICC = 0.79)。与 CCM 测量值相比,ACCM 测量值明显较低,这意味着模块间一致性较差。我们的结果表明,初学者进行定量角膜神经分析的表现可能可以接受,NFL 是最可靠的参数,而自动方法不能完全替代手动工作。