Am J Ophthalmol. 2021 Aug;228:212-219. doi: 10.1016/j.ajo.2021.03.048. Epub 2021 May 11.
The purpose of this study was to determine classification criteria for sympathetic ophthalmia.
Machine learning of cases with sympathetic ophthalmia and 5 other panuveitides.
Cases of panuveitides were collected in an informatics-designed preliminary database, and a final database was constructed of cases achieving supermajority agreement on the diagnosis using formal consensus techniques. Cases were split into a training set and a validation set. Machine learning using multinomial logistic regression was used in the training set to determine a parsimonious set of criteria that minimized the misclassification rate among the panuveitides. The resulting criteria were evaluated in the validation set.
A total of 1,012 cases of panuveitides, including 110 cases of sympathetic ophthalmia, were evaluated by machine learning. The overall accuracy for panuveitides was 96.3% in the training set and 94.0% in the validation set (95% confidence interval: 89.0-96.8). Key criteria for sympathetic ophthalmia included bilateral uveitis with 1) a history of unilateral ocular trauma or surgery and 2) an anterior chamber and vitreous inflammation or a panuveitis with choroidal involvement. The misclassification rates for sympathetic ophthalmia were 4.2% in the training set and 6.7% in the validation set.
The criteria for sympathetic ophthalmia had a low misclassification rate and appeared to perform sufficiently well for use in clinical and translational research.
本研究旨在确定交感眼炎的分类标准。
对患有交感眼炎和其他 5 种全葡萄膜炎的病例进行机器学习。
在信息学设计的初步数据库中收集全葡萄膜炎病例，并使用正式共识技术对诊断达成多数共识的病例构建最终数据库。病例分为训练集和验证集。使用多项逻辑回归的机器学习在训练集中用于确定一组简洁的标准，使全葡萄膜炎的分类错误率最小化。在验证集中评估由此产生的标准。
共评估了 1012 例全葡萄膜炎病例，其中包括 110 例交感眼炎病例。在训练集中，全葡萄膜炎的总体准确率为 96.3%，在验证集中为 94.0%（95%置信区间：89.0-96.8）。交感眼炎的关键标准包括双眼葡萄膜炎，伴有 1）单侧眼部创伤或手术史，和 2）前房和玻璃体炎症或伴有脉络膜受累的全葡萄膜炎。在训练集中，交感眼炎的分类错误率为 4.2%，在验证集中为 6.7%。
交感眼炎的标准分类错误率较低，似乎足以用于临床和转化研究。
