Smartphone-Based Pupillometry Using Machine Learning for the Diagnosis of Sports-Related Concussion
Anthony J Maxin, Bridget M Whelan, Michael R Levitt, Lynn B McGrath, Kimberly G Harmon, Dechang Chen · 2024
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Description
Anthony Maxin and co-authors test a phone-based pupil reader for sports concussion checks. Pupil response to light can be measured in numbers, which may give an objective sign of brain injury. College football players had their pupils read by a phone app at baseline. Seven traits of the light reflex were logged for each player.
Eleven players later suffered a concussion and were read again at diagnosis. Learning models tried all mixes of the seven traits to split the two states. The best model, a random forest on five traits, hit ninety-one percent accuracy after balancing the data. Raw data runs scored far worse, a gap the authors note with care. They see phone reads plus learning as a fast field tool.
Catalog record
| CREATOR | Anthony J Maxin, Bridget M Whelan, Michael R Levitt, Lynn B McGrath, Kimberly G Harmon, Dechang Chen |
|---|---|
| YEAR | 2024 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 35 KB |
| VERSION | 1.0 |
| LICENSE | CC BY 4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |