Am J Ophthalmol. 2021 Aug;228:174-181. doi: 10.1016/j.ajo.2021.03.056. Epub 2021 Apr 9.
To determine classification criteria for acute posterior multifocal placoid pigment epitheliopathy (APMPPE).
Machine learning of cases with APMPPE 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 82 cases of APMPPE, were evaluated by machine learning. Key criteria for APMPPE included (1) choroidal lesions with a plaque-like or placoid appearance and (2) characteristic imaging on fluorescein angiography (lesions "block early and stain late diffusely"). Overall accuracy for posterior uveitides was 92.7% in the training set and 98.0% (95% confidence interval 94.3, 99.3) in the validation set. The misclassification rates for APMPPE were 5% in the training set and 0% in the validation set.
The criteria for APMPPE had a low misclassification rate and seemed to perform sufficiently well for use in clinical and translational research.
确定急性后极部多灶性斑状色素上皮病变（APMPPE）的分类标准。
APMPPE 和其他 8 种后葡萄膜炎病例的机器学习。
在后葡萄膜炎的信息学设计的初步数据库中收集病例，并使用正式共识技术对达成诊断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上确定一组简洁的标准，这些标准最小化了后葡萄膜炎之间的分类错误率。在验证集上评估得出的标准。
通过机器学习评估了 1068 例后葡萄膜炎病例，包括 82 例 APMPPE。APMPPE 的关键标准包括（1）脉络膜病变呈斑块样或斑片状外观，（2）荧光素血管造影的特征性成像（病变“早期阻塞，晚期弥漫性染色”）。训练集的总体准确率为 92.7%，验证集为 98.0%（95%置信区间 94.3，99.3）。训练集的误诊率为 5%，验证集为 0%。
APMPPE 的标准误诊率较低，似乎足以用于临床和转化研究。
