Obtaining an objective measurement of the pain level of a patient has always been challenging for health care providers. The most common method of pain assessment in the hospital setting is asking the patients’ verbal ratings, which is considered to be a subjective approach. In order to get an objective pain level of a patient, we propose measuring pain level objectively using the pupillary response and machine learning algorithms. Thirty-two healthy subjects were enrolled in this study at Northeastern University. A painful stimulus was applied to healthy subjects by asking them to place their hands inside a bucket filled with iced water. We extracted 11 features from the pupil diameter data. To get the optimal subset of the features, a genetic algorithm (GA) was used to select features for the artificial neural network (ANN) classifier. Before feature selection, the f1-score of ANN was 54.0 ± 0.25% with all 11 features. After feature selection, ANN had the best performance with an accuracy of 81.0% using the selected feature subset, namely the Mean, the Root Mean Square (RMS), and the Pupillary Area Under Curve (PAUC). The experimental results suggested that pupillary response together with machine learning algorithms could be a promising method of objective pain level assessment. The outcomes of this study could improve patients’ experience of pain measurement in telehealthcare, especially during a pandemic when most people had to stay at home.

对于患者的疼痛程度的客观测量一直是医疗保健提供者所面临的挑战。在医院中，最常见的疼痛评估方法是询问患者的言语等级，这被认为是一种主观的方法。为了获得患者的客观疼痛程度，我们建议使用瞳孔反应和机器学习算法客观地测量疼痛程度。东北大学共有32名健康受试者参加了这项研究。通过要求健康的受试者将手放在盛有冰水的水桶中，从而施加了痛苦的刺激。我们从瞳孔直径数据中提取了11个特征。为了获得特征的最佳子集，使用遗传算法（GA）为人工神经网络（ANN）分类器选择特征。在特征选择之前，所有11个特征的ANN的f1得分均为54.0±0.25％。选择特征后，使用选定的特征子集即均值，均方根（RMS）和曲线下瞳孔面积（PAUC），ANN的最佳性能为81.0％。实验结果表明，瞳孔反应与机器学习算法一起可能是一种有希望的客观疼痛水平评估方法。这项研究的结果可以改善患者在远程医疗保健中进行疼痛测量的体验，尤其是在大流行期间，大多数人不得不待在家里。和曲线下瞳孔面积（PAUC）。实验结果表明，瞳孔反应与机器学习算法一起可能是一种有希望的客观疼痛水平评估方法。这项研究的结果可以改善患者在远程医疗保健中进行疼痛测量的体验，尤其是在大流行期间，大多数人不得不待在家里。和曲线下瞳孔面积（PAUC）。实验结果表明，瞳孔反应与机器学习算法一起可能是一种有希望的客观疼痛水平评估方法。这项研究的结果可以改善患者在远程医疗保健中进行疼痛测量的体验，尤其是在大流行期间，大多数人不得不待在家里。