Smartphone-Based Pupillometry Using Machine Learning for the Diagnosis of Sports-Related Concussion
<b>Background:</b> Quantitative pupillometry has been proposed as an objective means to diagnose acute sports-related concussion (SRC). <b>Objective:</b> To assess the diagnostic accuracy of a smartphone-based quantitative pupillometer in the acute diagnosis of SRC. <b>...
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MDPI AG
2024-12-01
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| Series: | Diagnostics |
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| Online Access: | https://www.mdpi.com/2075-4418/14/23/2723 |
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| author | Anthony J. Maxin Bridget M. Whelan Michael R. Levitt Lynn B. McGrath Kimberly G. Harmon |
| author_facet | Anthony J. Maxin Bridget M. Whelan Michael R. Levitt Lynn B. McGrath Kimberly G. Harmon |
| author_sort | Anthony J. Maxin |
| collection | DOAJ |
| description | <b>Background:</b> Quantitative pupillometry has been proposed as an objective means to diagnose acute sports-related concussion (SRC). <b>Objective:</b> To assess the diagnostic accuracy of a smartphone-based quantitative pupillometer in the acute diagnosis of SRC. <b>Methods:</b> 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. <b>Results</b>: 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%. <b>Conclusions</b>: Smartphone pupillometry in combination with machine learning may provide fast and objective SRC diagnosis in football athletes. |
| format | Article |
| id | doaj-art-067c622e297d4ec7bbbfb93edcefcf0e |
| institution | Kabale University |
| issn | 2075-4418 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Diagnostics |
| spelling | doaj-art-067c622e297d4ec7bbbfb93edcefcf0e2024-12-13T16:24:48ZengMDPI AGDiagnostics2075-44182024-12-011423272310.3390/diagnostics14232723Smartphone-Based Pupillometry Using Machine Learning for the Diagnosis of Sports-Related ConcussionAnthony J. Maxin0Bridget M. Whelan1Michael R. Levitt2Lynn B. McGrath3Kimberly G. Harmon4Department of Neurological Surgery, University of Washington, Seattle, WA 98195, USADepartment of Family Medicine, Sports Medicine Section, University of Washington, Seattle, WA 98195, USADepartment of Neurological Surgery, University of Washington, Seattle, WA 98195, USADepartment of Neurological Surgery, Northern Light Health, Portland, ME 04102, USADepartment of Family Medicine, Sports Medicine Section, University of Washington, Seattle, WA 98195, USA<b>Background:</b> Quantitative pupillometry has been proposed as an objective means to diagnose acute sports-related concussion (SRC). <b>Objective:</b> To assess the diagnostic accuracy of a smartphone-based quantitative pupillometer in the acute diagnosis of SRC. <b>Methods:</b> 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. <b>Results</b>: 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%. <b>Conclusions</b>: Smartphone pupillometry in combination with machine learning may provide fast and objective SRC diagnosis in football athletes.https://www.mdpi.com/2075-4418/14/23/2723smartphone pupillometrysports-related concussiondiagnosticsbiomarkerspupillary light reflexdigital health |
| spellingShingle | Anthony J. Maxin Bridget M. Whelan Michael R. Levitt Lynn B. McGrath Kimberly G. Harmon Smartphone-Based Pupillometry Using Machine Learning for the Diagnosis of Sports-Related Concussion Diagnostics smartphone pupillometry sports-related concussion diagnostics biomarkers pupillary light reflex digital health |
| title | Smartphone-Based Pupillometry Using Machine Learning for the Diagnosis of Sports-Related Concussion |
| title_full | Smartphone-Based Pupillometry Using Machine Learning for the Diagnosis of Sports-Related Concussion |
| title_fullStr | Smartphone-Based Pupillometry Using Machine Learning for the Diagnosis of Sports-Related Concussion |
| title_full_unstemmed | Smartphone-Based Pupillometry Using Machine Learning for the Diagnosis of Sports-Related Concussion |
| title_short | Smartphone-Based Pupillometry Using Machine Learning for the Diagnosis of Sports-Related Concussion |
| title_sort | smartphone based pupillometry using machine learning for the diagnosis of sports related concussion |
| topic | smartphone pupillometry sports-related concussion diagnostics biomarkers pupillary light reflex digital health |
| url | https://www.mdpi.com/2075-4418/14/23/2723 |
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