Explainable Machine Learning Predictions of Perceptual Sensitivity for Retinal Prostheses.
Explainable Machine Learning Predictions of Perceptual Sensitivity for Retinal Prostheses.
复制标题
视网膜假体感知敏感性的可解释机器学习预测。
DOI：
10.1101/2023.02.09.23285633
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发表时间：
2023
期刊：
影响因子：
--
通讯作者：
Beyeler,Michael
中科院分区：
文献类型：
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作者：
Pogoncheff,Galen;Hu,Zuying;Rokem,Ariel;Beyeler,Michael
目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戒律。尽管受试者之间、受试者内部的电极之间以及随着时间的推移，视网膜假体使用者的知觉阈值存在很大差异，但使用者必须经历“系统匹配”，这是一个根据受试者的知觉阈值校准刺激参数的过程。为了解决这些挑战，我们(1)将机器学习模型与大型纵向数据集相匹配，以预测单个电极阈值和失活作为刺激、电极、我们的模型解释了感知阈值响应方差的76%，并能够通过F1和ROC曲线下面积分别高达0.732和0.911的分数来预测给定试验中电极是否被停用。我们的模型确定了感知敏感度的新预测因子，包括受试者年龄、失明起病时间和电极-中心凹距离。显著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提供基于XAI的阈值预测策略，这有可能改变预测视觉结果的临床实践。
ObjectiveRetinal 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.ApproachTo 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 resultsOur 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.SignificanceOur 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.