Classification Criteria for Punctate Inner Choroiditis.
Classification Criteria for Punctate Inner Choroiditis.
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DOI：
10.1016/j.ajo.2021.03.046
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发表时间：
2021-08
影响因子：
4.2
通讯作者：
Standardization of Uveitis Nomenclature (SUN) Working Group
中科院分区：
文献类型：
--
作者：
Standardization of Uveitis Nomenclature (SUN) Working Group
确定点状内脉络膜炎（PIC）的分类标准。机器学习PIC和其他8种后葡萄膜炎病例。后葡萄膜炎病例收集在信息学设计的初步数据库中，最终数据库的构建采用正式的共识技术，在诊断上达到绝大多数一致的病例。将病例分为训练集和验证集。在训练集上使用使用多项逻辑回归的机器学习来确定一组最小化后葡萄膜中的错误分类率的简约标准。在验证集上评价所得标准。通过机器学习评估了1068例后葡萄膜炎，包括144例PIC。事先知情同意的关键标准包括：1）直径<250 μm的“点状”脉络膜斑点; 2）无至极轻微的前房和玻璃体炎症; 3）累及后极，有或无中周边。后葡萄膜炎的总体准确度在训练集中为93.9%，在验证集中为98.0%（95%置信区间94.3，99.3）。PIC的错误分类率在训练集中为15%，在验证集中为9%。PIC的标准有一个合理的低错误分类率，并表现出足够好的临床和转化研究中使用。使用一种形式化的方法来制定分类标准，包括基于信息学的病例收集、基于共识技术的病例选择和机器学习，制定了点状内脉络膜炎的分类标准。关键标准包括直径<250 μm的“点状”脉络膜病变，无至极轻微的前房和玻璃体炎症，以及后极和/或中周边脉络膜视网膜受累。由此产生的分类标准有一个低的错误分类率。
To determine classification criteria for punctate inner choroiditis (PIC). Machine learning of cases with PIC and 8 other posterior uveitides. Cases of posterior uveitides were collected in an informatics-designed preliminary database, and a final database was constructed of cases achieving supermajority agreement on diagnosis, using formal consensus techniques. Cases were split into a training set and a validation set. Machine learning using multinomial logistic regression was used on the training set to determine a parsimonious set of criteria that minimized the misclassification rate among the posterior uveitides. The resulting criteria were evaluated on the validation set. One thousand sixty-eight cases of posterior uveitides, including 144 cases of PIC, were evaluated by machine learning. Key criteria for PIC included: 1) “punctate” appearing choroidal spots <250 μm in diameter; 2) absent to minimal anterior chamber and vitreous inflammation; and 3) involvement of the posterior pole with or without mid-periphery. Overall accuracy for posterior uveitides was 93.9% in the training set and 98.0% (95% confidence interval 94.3, 99.3) in the validation set. The misclassification rates for PIC were 15% in the training set and 9% in the validation set. The criteria for PIC had a reasonably low misclassification rate and appeared to perform sufficiently well for use in clinical and translational research. Using a formalized approach to developing classification criteria, including informatics-based case collection, consensus-technique-based case selection, and machine learning, classification criteria for punctate inner choroiditis were developed. Key criteria included “punctate”-appearing choroidal lesions <250 μm in diameter, absent to minimal anterior chamber and vitreous inflammation, and posterior pole and/or mid periphery chorioretinal involvement. The resulting classification criteria had a low misclassification rate.
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影响因子：
1.7
作者：
Trusko B;Thorne J;Jabs D;Belfort R;Dick A;Gangaputra S;Nussenblatt R;Okada A;Rosenbaum J;Standardization of Uveitis Nomenclature (SUN) Project
通讯作者：
Standardization of Uveitis Nomenclature (SUN) Project
DOI：
10.1097/iae.0000000000000125
发表时间：
2014-08-01
影响因子：
3.3
作者：
Leung, Theresa G.;Moradi, Ahmadreza;Thorne, Jennifer E.
通讯作者：
Thorne, Jennifer E.
影响因子：
--
作者：
Essex, Rohan W.;Wong, James;Dowler, Jonathan
通讯作者：
Dowler, Jonathan
影响因子：
1
作者：
Kim, Esther Lee;Thanos, Aristomenis;Faia, Lisa J.
通讯作者：
Faia, Lisa J.
影响因子：
13.7
作者：
Olsen, TW;Capone, A;Aaberg, TM
通讯作者：
Aaberg, TM