Perceptual Learning Based on the Lateral Masking Paradigm in Anisometropic Amblyopia With or Without a Patching History.

Purpose: Perceptual learning (PL) has shown promising performance in restoring visual function in adolescent amblyopes. We retrospectively compared the effect of a well-accepted PL paradigm on patients with anisometropic amblyopia with or without a patching therapy history (patching therapy [PT] group versus no patching therapy [NPT] group). Methods: Eighteen PT and 13 NPT patients with anisometropic amblyopia underwent monocular PL for 3 months. During training, patients practiced a Gabor detection task following the lateral masking paradigm by applying a temporal two-alternative forced choice procedure with the amblyopic eye. Monocular contrast sensitivity functions (CSF), visual acuity, interocular differences in visual function metrics, and stereoacuity were compared before and after training. Results: PL improved the visual acuity of the amblyopia eyes by 0.5 lines on average in the PT group and 1.5 lines in the NPT group. A significant reduction in the interocular difference in visual acuity was observed in the NPT group (P < 0.01) but not in the PT group (P = 0.05). Regarding CSF metrics, the area under the log CSF and cutoff in the amblyopic eyes of the NPT groups increased after training (P < 0.05). In addition, the interocular differences of the CSF metrics (P < 0.05) in the NPT group were significantly reduced. However, in the PT group, all the CSF metrics were unchanged after training. A total of 27 of 31 patients in both groups had no measurable stereopsis pretraining, and recovery after training was not significant. Conclusions: PL based on a lateral masking training paradigm improved visual function in anisometropic amblyopia. Patients without a patching history achieved greater benefits. Translational Relevance: PL based on a lateral masking training paradigm could be a new treatment for amblyopia.

Novel Quantitative Contrast Sensitivity Function Enhances the Prediction of Treatment Outcome and Recurrence in Amblyopia.

Purpose: 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. Methods: 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. Results: 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. Conclusions: 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.