Maxin Anthony J, Whelan Bridget M, Levitt Michael R, McGrath Lynn B, Harmon Kimberly G
Department of Neurological Surgery, University of Washington, Seattle, WA 98195, USA.
School of Medicine, Creighton University, Omaha, NE 68178, USA.
Diagnostics (Basel). 2024 Dec 3;14(23):2723. doi: 10.3390/diagnostics14232723.
Quantitative pupillometry has been proposed as an objective means to diagnose acute sports-related concussion (SRC). To assess the diagnostic accuracy of a smartphone-based quantitative pupillometer in the acute diagnosis of SRC. Division I college football players had baseline pupillometry including pupillary light reflex (PLR) parameters of maximum resting diameter, minimum diameter after light stimulus, percent change in pupil diameter, latency of pupil constriction onset, mean constriction velocity, maximum constriction velocity, and mean dilation velocity using a smartphone-based app. When an SRC occurred, athletes had the smartphone pupillometry repeated as part of their concussion testing. All combinations of the seven PLR parameters were tested in machine learning binary classification models to determine the optimal combination for differentiating between non-concussed and concussed athletes. : 93 football athletes underwent baseline pupillometry testing. Among these athletes, 11 suffered future SRC and had pupillometry recordings repeated at the time of diagnosis. In the machine learning pupillometry analysis that used the synthetic minority oversampling technique to account for the significant class imbalance in our dataset, the best-performing model was a random forest algorithm with the combination of latency, maximum diameter, minimum diameter, mean constriction velocity, and maximum constriction velocity PLR parameters as feature inputs. This model produced 91% overall accuracy, 98% sensitivity, 84.2% specificity, area under the curve (AUC) of 0.91, and an F1 score of 91.6% in differentiating between baseline and SRC recordings. In the machine learning analysis prior to oversampling of our imbalanced dataset, the best-performing model was k-nearest neighbors using latency, maximum diameter, maximum constriction velocity, and mean dilation velocity to produce 82% accuracy, 40% sensitivity, 87% specificity, AUC of 0.64, and F1 score of 24%. : Smartphone pupillometry in combination with machine learning may provide fast and objective SRC diagnosis in football athletes.
定量瞳孔测量法已被提议作为诊断急性运动相关脑震荡（SRC）的一种客观方法。为评估基于智能手机的定量瞳孔测量仪在SRC急性诊断中的诊断准确性。一级大学橄榄球运动员使用基于智能手机的应用程序进行基线瞳孔测量，包括瞳孔光反射（PLR）参数，如最大静息直径、光刺激后的最小直径、瞳孔直径变化百分比、瞳孔收缩起始潜伏期、平均收缩速度、最大收缩速度和平均扩张速度。当发生SRC时，运动员作为脑震荡测试的一部分重复进行智能手机瞳孔测量。在机器学习二元分类模型中测试了七个PLR参数的所有组合，以确定区分未发生脑震荡和发生脑震荡运动员的最佳组合。：93名橄榄球运动员接受了基线瞳孔测量测试。在这些运动员中，11人后来发生了SRC，并在诊断时重复进行了瞳孔测量记录。在使用合成少数过采样技术来解决我们数据集中显著的类别不平衡问题的机器学习瞳孔测量分析中，表现最佳的模型是随机森林算法，其将潜伏期、最大直径、最小直径、平均收缩速度和最大收缩速度PLR参数组合作为特征输入。该模型在区分基线和SRC记录时，总体准确率为91%，灵敏度为98%，特异性为84.2%，曲线下面积（AUC）为0.91，F1分数为91.6%。在对我们的不平衡数据集进行过采样之前的机器学习分析中，表现最佳的模型是k近邻算法，使用潜伏期、最大直径、最大收缩速度和平均扩张速度，准确率为82%，灵敏度为40%，特异性为87%，AUC为0.64，F1分数为24%。：智能手机瞳孔测量法与机器学习相结合可能为橄榄球运动员提供快速、客观的SRC诊断。
