Am J Ophthalmol. 2021 Aug;228:268-274. doi: 10.1016/j.ajo.2021.03.045. Epub 2021 Apr 15.
To determine classification criteria for pars planitis.
Machine learning of cases with pars planitis and 4 other intermediate uveitides.
Cases of intermediate uveitides 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 on the training set to determine a parsimonious set of criteria that minimized the misclassification rate among the intermediate uveitides. The resulting criteria were evaluated on the validation set.
Five hundred eighty-nine cases of intermediate uveitides, including 226 cases of pars planitis, were evaluated by machine learning. The overall accuracy for intermediate uveitides was 99.8% in the training set and 99.3% in the validation set (95% confidence interval 96.1, 99.9). Key criteria for pars planitis included unilateral or bilateral intermediate uveitis with either 1) snowballs in the vitreous or 2) snowbanks on the pars plana. Key exclusions included: 1) multiple sclerosis, 2) sarcoidosis, and 3) syphilis. The misclassification rates for pars planitis were 0% in the training set and 1.7% in the validation set, respectively.
The criteria for pars planitis had a low misclassification rate and appeared to perform sufficiently well for use in clinical and translational research.
确定中间葡萄膜炎中 pars 燐光炎的分类标准。
使用机器学习对 pars 燐光炎和其他 4 种中间葡萄膜炎病例进行分析。
通过信息学设计的初步数据库收集中间葡萄膜炎病例，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。将病例分为训练集和验证集。使用多项逻辑回归对训练集进行机器学习，以确定一组简化的标准，使中间葡萄膜炎的分类错误率最小化。然后在验证集上评估得出的标准。
对 589 例中间葡萄膜炎病例（包括 226 例 pars 燐光炎）进行了机器学习评估。训练集中中间葡萄膜炎的总体准确率为 99.8%，验证集中为 99.3%（95%置信区间 96.1，99.9）。 pars 燐光炎的关键标准包括单侧或双侧中间葡萄膜炎，伴有 1）玻璃体雪球状混浊或 2）后极部雪堤样改变。关键排除标准包括：1）多发性硬化症，2）结节病，和 3）梅毒。训练集中 pars 燐光炎的分类错误率为 0%，验证集中为 1.7%。
pars 燐光炎的分类标准错误率较低，似乎足以用于临床和转化研究。
