PDF(Pubmed)
Abstract：
UNASSIGNED: Although effective amblyopia treatments are available, treatment outcome is unpredictable, and the condition recurs in up to 25% of the patients. We aimed to evaluate whether a large-scale quantitative contrast sensitivity function (CSF) data source, coupled with machine learning (ML) algorithms, can predict amblyopia treatment response and recurrence in individuals.
UNASSIGNED: Visual function measures from traditional chart vision acuity (VA) and novel CSF assessments were used as the main predictive variables in the models. Information from 58 potential predictors was extracted to predict treatment response and recurrence. Six ML methods were applied to construct models. The SHapley Additive exPlanations was used to explain the predictions.
UNASSIGNED: A total of 2559 consecutive records of 643 patients with amblyopia were eligible for modeling. Combining variables from VA and CSF assessments gave the highest accuracy for treatment response prediction, with the area under the receiver operating characteristic curve (AUC) of 0.863 and 0.815 for outcome predictions after 3 and 6 months, respectively. Variables from the VA assessment alone predicted the treatment response, with AUC values of 0.723 and 0.675 after 3 and 6 months, respectively. Variables from the CSF assessment gave rise to an AUC of 0.909 for recurrence prediction compared to 0.539 for VA assessment alone, and adding VA variables did not improve predictive performance. The interocular differences in CSF features are significant contributors to recurrence risk.
UNASSIGNED: Our models showed CSF data could enhance treatment response prediction and accurately predict amblyopia recurrence, which has the potential to guide amblyopia management by enabling patient-tailored decision making.
UNASSIGNED: Visual function measures from traditional chart vision acuity (VA) and novel CSF assessments were used as the main predictive variables in the models. Information from 58 potential predictors was extracted to predict treatment response and recurrence. Six ML methods were applied to construct models. The SHapley Additive exPlanations was used to explain the predictions.
UNASSIGNED: A total of 2559 consecutive records of 643 patients with amblyopia were eligible for modeling. Combining variables from VA and CSF assessments gave the highest accuracy for treatment response prediction, with the area under the receiver operating characteristic curve (AUC) of 0.863 and 0.815 for outcome predictions after 3 and 6 months, respectively. Variables from the VA assessment alone predicted the treatment response, with AUC values of 0.723 and 0.675 after 3 and 6 months, respectively. Variables from the CSF assessment gave rise to an AUC of 0.909 for recurrence prediction compared to 0.539 for VA assessment alone, and adding VA variables did not improve predictive performance. The interocular differences in CSF features are significant contributors to recurrence risk.
UNASSIGNED: Our models showed CSF data could enhance treatment response prediction and accurately predict amblyopia recurrence, which has the potential to guide amblyopia management by enabling patient-tailored decision making.
摘要：
■尽管有有效的弱视治疗方法，治疗结果不可预测，这种情况在高达25%的患者中复发。我们的目的是评估大规模定量对比敏感度函数(CSF)数据源，再加上机器学习(ML)算法，可以预测弱视个体的治疗反应和复发。
■来自传统图表视敏度（VA）和新型CSF评估的视觉功能测量值被用作模型中的主要预测变量。从58个潜在预测因子中提取信息来预测治疗反应和复发。应用六种ML方法构建模型。Shapley添加剂扩张被用来解释预测。
■总共2559个连续记录的643例弱视患者符合建模条件。结合来自VA和CSF评估的变量对治疗反应预测的准确性最高。受试者工作特征曲线下面积（AUC）为0.863和0.815，用于3个月和6个月后的结果预测，分别。仅VA评估的变量就可以预测治疗反应，3个月和6个月后的AUC值为0.723和0.675，分别。CSF评估的变量导致复发预测的AUC为0.909，而单独的VA评估为0.539。添加VA变量并不能提高预测性能。CSF特征的眼间差异是复发风险的重要原因。
■我们的模型显示CSF数据可以增强治疗反应预测并准确预测弱视复发，它有可能通过为患者量身定制的决策来指导弱视管理。
■来自传统图表视敏度（VA）和新型CSF评估的视觉功能测量值被用作模型中的主要预测变量。从58个潜在预测因子中提取信息来预测治疗反应和复发。应用六种ML方法构建模型。Shapley添加剂扩张被用来解释预测。
■总共2559个连续记录的643例弱视患者符合建模条件。结合来自VA和CSF评估的变量对治疗反应预测的准确性最高。受试者工作特征曲线下面积（AUC）为0.863和0.815，用于3个月和6个月后的结果预测，分别。仅VA评估的变量就可以预测治疗反应，3个月和6个月后的AUC值为0.723和0.675，分别。CSF评估的变量导致复发预测的AUC为0.909，而单独的VA评估为0.539。添加VA变量并不能提高预测性能。CSF特征的眼间差异是复发风险的重要原因。
■我们的模型显示CSF数据可以增强治疗反应预测并准确预测弱视复发，它有可能通过为患者量身定制的决策来指导弱视管理。
