Department of Ophthalmology, Tokyo Medical University Hospital, Tokyo, Japan.
Department of Ophthalmology, Tokyo Medical University Hospital, Tokyo, Japan.
Ophthalmology. 2021 Aug;128(8):1197-1208. doi: 10.1016/j.ophtha.2021.01.019. Epub 2021 Jan 21.
Various immune mediators have crucial roles in the pathogenesis of intraocular diseases. Machine learning can be used to automatically select and weigh various predictors to develop models maximizing predictive power. However, these techniques have not yet been applied extensively in studies focused on intraocular diseases. We evaluated whether 5 machine learning algorithms applied to the data of immune-mediator levels in aqueous humor can predict the actual diagnoses of 17 selected intraocular diseases and identified which immune mediators drive the predictive power of a machine learning model.
Cross-sectional study.
Five hundred twelve eyes with diagnoses from among 17 intraocular diseases.
Aqueous humor samples were collected, and the concentrations of 28 immune mediators were determined using a cytometric bead array. Each immune mediator was ranked according to its importance using 5 machine learning algorithms. Stratified k-fold cross-validation was used in evaluation of algorithms with the dataset divided into training and test datasets.
The algorithms were evaluated in terms of precision, recall, accuracy, F-score, area under the receiver operating characteristic curve, area under the precision-recall curve, and mean decrease in Gini index.
Among the 5 machine learning models, random forest (RF) yielded the highest classification accuracy in multiclass differentiation of 17 intraocular diseases. The RF prediction models for vitreoretinal lymphoma, acute retinal necrosis, endophthalmitis, rhegmatogenous retinal detachment, and primary open-angle glaucoma achieved the highest classification accuracy, precision, and recall. Random forest recognized vitreoretinal lymphoma, acute retinal necrosis, endophthalmitis, rhegmatogenous retinal detachment, and primary open-angle glaucoma with the top 5 F-scores. The 3 highest-ranking relevant immune mediators were interleukin (IL)-10, interferon-γ-inducible protein (IP)-10, and angiogenin for prediction of vitreoretinal lymphoma; monokine induced by interferon γ, interferon γ, and IP-10 for acute retinal necrosis; and IL-6, granulocyte colony-stimulating factor, and IL-8 for endophthalmitis.
Random forest algorithms based on 28 immune mediators in aqueous humor successfully predicted the diagnosis of vitreoretinal lymphoma, acute retinal necrosis, and endophthalmitis. Overall, the findings of the present study contribute to increased knowledge on new biomarkers that potentially can facilitate diagnosis of intraocular diseases in the future.
各种免疫介质在眼内疾病的发病机制中起着关键作用。机器学习可用于自动选择和权衡各种预测因子，以开发最大程度提高预测能力的模型。然而，这些技术尚未广泛应用于针对眼内疾病的研究。我们评估了 5 种机器学习算法应用于房水中免疫介质水平的数据是否可以预测 17 种选定的眼内疾病的实际诊断，并确定哪些免疫介质驱动机器学习模型的预测能力。
横断面研究。
来自 17 种眼内疾病的 512 只眼。
采集房水样本，使用细胞因子微珠阵列测定 28 种免疫介质的浓度。使用 5 种机器学习算法根据每种免疫介质的重要性对其进行排序。使用分层 k 折交叉验证评估算法，数据集分为训练数据集和测试数据集。
算法的准确性、召回率、精度、F 分数、受试者工作特征曲线下面积、精度-召回曲线下面积和基尼指数平均减少量。
在 5 种机器学习模型中，随机森林（RF）在 17 种眼内疾病的多类分化中具有最高的分类准确性。RF 预测模型对玻璃体视网膜淋巴瘤、急性视网膜坏死、眼内炎、孔源性视网膜脱离和原发性开角型青光眼的分类准确性、精度和召回率最高。随机森林识别玻璃体视网膜淋巴瘤、急性视网膜坏死、眼内炎、孔源性视网膜脱离和原发性开角型青光眼的前 5 个 F 分数最高。预测玻璃体视网膜淋巴瘤的前 3 个相关免疫介质为白细胞介素（IL）-10、干扰素-γ诱导蛋白（IP）-10 和血管生成素；预测急性视网膜坏死的前 3 个相关免疫介质为干扰素γ诱导的单核因子、干扰素γ和 IP-10；预测眼内炎的前 3 个相关免疫介质为白细胞介素（IL）-6、粒细胞集落刺激因子和白细胞介素（IL）-8。
基于房水中 28 种免疫介质的随机森林算法成功预测了玻璃体视网膜淋巴瘤、急性视网膜坏死和眼内炎的诊断。总体而言，本研究的结果有助于增加对新生物标志物的认识，这些标志物有可能有助于未来眼内疾病的诊断。
