Am J Ophthalmol. 2021 Aug;228:182-191. doi: 10.1016/j.ajo.2021.03.039. Epub 2021 May 11.
To determine classification criteria for syphilitic uveitis.
Machine learning of cases with syphilitic uveitis and 24 other uveitides.
Cases of anterior, intermediate, posterior, and 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 analyzed by anatomic class, and each class was 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 different uveitic classes. The resulting criteria were evaluated on the validation set.
Two hundred twenty-two cases of syphilitic uveitis were evaluated by machine learning, with cases evaluated against other uveitides in the relevant uveitic class. Key criteria for syphilitic uveitis included a compatible uveitic presentation (anterior uveitis; intermediate uveitis; or posterior or panuveitis with retinal, retinal pigment epithelial, or retinal vascular inflammation) and evidence of syphilis infection with a positive treponemal test. The Centers for Disease Control and Prevention reverse screening algorithm for syphilis testing is recommended. The misclassification rates for syphilitic uveitis in the training sets were as follows: anterior uveitides 0%, intermediate uveitides 6.0%, posterior uveitides 0%, panuveitides 0%, and infectious posterior/panuveitides 8.6%. The overall accuracy of the diagnosis of syphilitic uveitis in the validation set was 100% (99% confidence interval 99.5, 100)-that is, the validation set's misclassification rates were 0% for each uveitic class.
The criteria for syphilitic uveitis had a low misclassification rate and seemed to perform sufficiently well for use in clinical and translational research.
确定梅毒性葡萄膜炎的分类标准。
对梅毒性葡萄膜炎和 24 种其他葡萄膜炎病例进行机器学习。
在一个信息学设计的初步数据库中收集前葡萄膜炎、中间葡萄膜炎、后葡萄膜炎和全葡萄膜炎病例，然后使用正式共识技术对达成诊断的大多数病例构建最终数据库。根据解剖分类对病例进行分析，每个类别分为训练集和验证集。使用多项逻辑回归的机器学习对训练集进行分析，以确定一组尽可能少的标准，使不同葡萄膜炎类别的分类错误率最小化。将得出的标准应用于验证集进行评估。
通过机器学习对 222 例梅毒性葡萄膜炎病例进行评估，在相关葡萄膜炎类别中对病例进行与其他葡萄膜炎的比较。梅毒性葡萄膜炎的关键标准包括相容的葡萄膜炎表现（前葡萄膜炎；中间葡萄膜炎；或后葡萄膜炎或全葡萄膜炎伴视网膜、视网膜色素上皮或视网膜血管炎症）和梅毒感染的证据，包括阳性梅毒螺旋体检测。建议使用美国疾病控制与预防中心梅毒检测反向筛查算法。训练集的梅毒性葡萄膜炎分类错误率如下：前葡萄膜炎 0%，中间葡萄膜炎 6.0%，后葡萄膜炎 0%，全葡萄膜炎 0%，感染性后葡萄膜炎/全葡萄膜炎 8.6%。验证集诊断梅毒性葡萄膜炎的总体准确率为 100%（99%置信区间 99.5，100），即每个葡萄膜炎类别的验证集分类错误率均为 0%。
梅毒性葡萄膜炎的标准分类错误率较低，似乎足以用于临床和转化研究。
