Keratoconus detection using OCT corneal and epithelial thickness map parameters and patterns.

Keratoconus detection using OCT corneal and epithelial thickness map parameters and patterns.
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使用 OCT 角膜和上皮厚度图参数和模式进行圆锥角膜检测

DOI:
10.1097/j.jcrs.0000000000000498
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发表时间:
2021-06-01
影响因子:
2.8
通讯作者:
Li Y
Li Y
中科院分区:
医学2区
文献类型:
--
作者:
Yang Y;Pavlatos E;Chamberlain W;Huang D;Li Y

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提供了一种两步决策树方法,可使用 OCT 角膜和上皮厚度图参数和模式高精度检测圆锥角膜。目的:使用光学相干断层扫描 (OCT) 角膜图参数和模式检测圆锥角膜。地点:俄勒冈州波特兰俄勒冈健康与科学大学凯西眼科研究所。设计:横断面观察研究。方法:使用谱域 OCT 获取正常、明显圆锥形、亚临床圆锥形和截头圆锥形 (FFK) 眼的角膜和上皮厚度图。设计了两步决策树。如果满足两个决策树条件,则眼睛将被分类为圆锥角膜。首先,4 个定量角膜厚度(最小、最小-最大和鼻上-颞下)和上皮厚度(标准差)图参数中的至少 1 个超过截止值。其次,通过目视检查,上皮厚度图上存在同心变薄模式,并且角膜和上皮厚度图上存在重合变薄模式。结果:该研究包括来自 29 名正常参与者的 54 只眼睛、来自 65 名患者的 91 只明显圆锥角膜眼、来自 11 名患者的 12 只亚临床圆锥角膜眼和来自 19 名患者的 19 只 FFK 眼。决策树正确分类了所有正常眼睛(100% 特异性),并且对检测明显圆锥角膜 (97.8%)、亚临床圆锥角膜 (100.0%) 和 FFK (73.7%) 具有良好的灵敏度。结论:两步决策树提供了检测圆锥角膜的有用工具,包括早期疾病阶段的病例(亚临床圆锥角膜和 FFK)。 OCT 角膜和上皮厚度图参数和模式可与地形图结合使用,以改善圆锥角膜筛查。
A 2-step decision tree method is provided to detect keratoconus with high accuracy using OCT corneal and epithelial thickness map parameters and patterns. Purpose: To detect keratoconus using optical coherence tomography (OCT) corneal map parameters and patterns. Setting: Casey Eye Institute, Oregon Health and Science University, Portland, Oregon. Design: Cross-sectional observational study. Methods: A spectral-domain OCT was used to acquire corneal and epithelial thickness maps in normal, manifest keratoconic, subclinical keratoconic, and forme fruste keratoconic (FFK) eyes. A 2-step decision tree was designed. An eye will be classified as keratoconus if both decision tree conditions are met. First, at least 1 of the 4 quantitative corneal thickness (minimum, minimum–maximum, and superonasal–inferotemporal) and epithelial thickness (standard deviation) map parameters exceed cutoff values. Second, presence of both concentric thinning pattern on the epithelial thickness map and coincident thinning patterns on corneal and epithelial thickness maps by visual inspection. Results: The study comprised 54 eyes from 29 normal participants, 91 manifest keratoconic eyes from 65 patients, 12 subclinical keratoconic eyes from 11 patients, and 19 FFK eyes from 19 patients. The decision tree correctly classified all normal eyes (100% specificity) and had good sensitivities for detecting manifest keratoconus (97.8%), subclinical keratoconus (100.0%), and FFK (73.7%). Conclusions: The 2-step decision tree provided a useful tool to detect keratoconus, including cases at early disease stages (subclinical keratoconus and FFK). OCT corneal and epithelial thickness map parameters and patterns can be used in conjunction with topography to improve keratoconus screening.
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