Objective. Retinal prostheses evoke visual precepts by electrically stimulating functioning cells in the retina. Despite high variance in perceptual thresholds across subjects, among electrodes within a subject, and over time, retinal prosthesis users must undergo ‘system fitting’, a process performed to calibrate stimulation parameters according to the subject’s perceptual thresholds. Although previous work has identified electrode-retina distance and impedance as key factors affecting thresholds, an accurate predictive model is still lacking. Approach. To address these challenges, we (1) fitted machine learning models to a large longitudinal dataset with the goal of predicting individual electrode thresholds and deactivation as a function of stimulus, electrode, and clinical parameters (‘predictors’) and (2) leveraged explainable artificial intelligence (XAI) to reveal which of these predictors were most important. Main results. Our models accounted for up to 76% of the perceptual threshold response variance and enabled predictions of whether an electrode was deactivated in a given trial with F1 and area under the ROC curve scores of up to 0.732 and 0.911, respectively. Our models identified novel predictors of perceptual sensitivity, including subject age, time since blindness onset, and electrode-fovea distance. Significance. Our results demonstrate that routinely collected clinical measures and a single session of system fitting might be sufficient to inform an XAI-based threshold prediction strategy, which has the potential to transform clinical practice in predicting visual outcomes.

中文翻译：

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视网膜假体感知灵敏度的可解释机器学习预测

客观的。视网膜假体通过电刺激视网膜中的功能细胞来唤起视觉规则。尽管受试者之间、受试者内的电极之间的感知阈值存在很大差异，并且随着时间的推移，视网膜假体用户必须进行“系统拟合”，这是根据受试者的感知阈值校准刺激参数的过程。尽管之前的工作已将电极-视网膜距离和阻抗确定为影响阈值的关键因素，但仍然缺乏准确的预测模型。方法。为了应对这些挑战，我们（1）将机器学习模型拟合到大型纵向数据集，目的是预测单个电极阈值和失活作为刺激、电极和临床参数（“预测变量”）的函数，以及（2）利用可解释的人工智能 (XAI) 揭示这些预测因素中哪些是最重要的。主要结果。我们的模型占感知阈值响应方差的高达 76%，并能够预测在给定试验中电极是否停用，F1 和 ROC 曲线下面积分数分别高达 0.732 和 0.911。我们的模型确定了感知敏感性的新预测因子，包括受试者年龄、失明后的时间以及电极-中央凹距离。意义。我们的结果表明，常规收集的临床测量和单次系统拟合可能足以为基于 XAI 的阈值预测策略提供信息，该策略有可能改变预测视觉结果的临床实践。