Keck School of Medicine of University of Southern California, University of Southern California, Los Angeles, California, USA.
Department of Ophthalmology, University of Washington, Seattle, Washington, USA.
Ocul Immunol Inflamm. 2022 Feb 17;30(2):357-363. doi: 10.1080/09273948.2022.2028289.
The objective grading of anterior chamber inflammation (ACI) has remained a challenge in the field of uveitis. While the grading criteria produced by the Standardization of Uveitis Nomenclature (SUN) International Workshop have been widely adopted, limitations exist including interobserver variability and grading confined to discrete categories rather than a continuous measurement. Since the earliest iterations of optical coherence tomography (OCT), ACI has been assessed using anterior segment OCT and shown to correlate with slit-lamp findings. However, widespread use of this approach has not been adopted. Barriers to standardization include variability in OCT devices across clinical settings, lack of standardization of image acquisition protocols, varying quantification methods, and the difficulty of distinguishing inflammatory cells from other cell types. Modern OCT devices and techniques in artificial intelligence show promise in expanding the clinical applicability of anterior segment OCT for the grading of ACI.
眼前房炎症(ACI)的客观分级一直是葡萄膜炎领域的一个挑战。虽然由国际葡萄膜炎命名标准(SUN)工作组制定的分级标准已被广泛采用,但仍存在一些局限性,包括观察者间的变异性以及分级仅限于离散类别而不是连续测量。自光学相干断层扫描(OCT)的早期迭代以来,人们一直使用眼前节 OCT 来评估 ACI,并证实其与裂隙灯检查结果相关。然而,这种方法并未被广泛采用。标准化的障碍包括临床环境中 OCT 设备的变异性、图像采集协议缺乏标准化、不同的定量方法以及区分炎症细胞与其他细胞类型的困难。人工智能的现代 OCT 设备和技术有望扩大眼前节 OCT 在 ACI 分级中的临床适用性。