Uzma Samadani, Meng Li, Meng Qian, Eugene Laska, Robert Ritlop, Radek Kolecki, Marleen Reyes, Lindsey Altomare, Je Yeong Sone, Aylin Adem, Paul Huang, Douglas Kondziolka, Stephen Wall, Spiros Frangos, Charles Marmar
{"title":"基于眼动追踪的脑震荡生物标志物的敏感性和特异性。","authors":"Uzma Samadani, Meng Li, Meng Qian, Eugene Laska, Robert Ritlop, Radek Kolecki, Marleen Reyes, Lindsey Altomare, Je Yeong Sone, Aylin Adem, Paul Huang, Douglas Kondziolka, Stephen Wall, Spiros Frangos, Charles Marmar","doi":"10.2217/cnc.15.3","DOIUrl":null,"url":null,"abstract":"<p><strong>Object: </strong>The purpose of the current study is to determine the sensitivity and specificity of an eye tracking method as a classifier for identifying concussion.</p><p><strong>Methods: </strong>Brain injured and control subjects prospectively underwent both eye tracking and Sport Concussion Assessment Tool 3. The results of eye tracking biomarker based classifier models were then validated against a dataset of individuals not used in building a model. The area under the curve (AUC) of receiver operating characteristics was examined.</p><p><strong>Results: </strong>An optimal classifier based on best subset had an AUC of 0.878, and a cross-validated AUC of 0.852 in CT- subjects and an AUC of 0.831 in a validation dataset. The optimal misclassification rate in an external dataset (n = 254) was 13%.</p><p><strong>Conclusion: </strong>If one defines concussion based on history, examination, radiographic and Sport Concussion Assessment Tool 3 criteria, it is possible to generate an eye tracking based biomarker that enables detection of concussion with reasonably high sensitivity and specificity.</p>","PeriodicalId":37006,"journal":{"name":"Concussion","volume":"1 1","pages":"CNC3"},"PeriodicalIF":0.0000,"publicationDate":"2015-08-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.2217/cnc.15.3","citationCount":"36","resultStr":"{\"title\":\"Sensitivity and specificity of an eye movement tracking-based biomarker for concussion.\",\"authors\":\"Uzma Samadani, Meng Li, Meng Qian, Eugene Laska, Robert Ritlop, Radek Kolecki, Marleen Reyes, Lindsey Altomare, Je Yeong Sone, Aylin Adem, Paul Huang, Douglas Kondziolka, Stephen Wall, Spiros Frangos, Charles Marmar\",\"doi\":\"10.2217/cnc.15.3\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Object: </strong>The purpose of the current study is to determine the sensitivity and specificity of an eye tracking method as a classifier for identifying concussion.</p><p><strong>Methods: </strong>Brain injured and control subjects prospectively underwent both eye tracking and Sport Concussion Assessment Tool 3. The results of eye tracking biomarker based classifier models were then validated against a dataset of individuals not used in building a model. The area under the curve (AUC) of receiver operating characteristics was examined.</p><p><strong>Results: </strong>An optimal classifier based on best subset had an AUC of 0.878, and a cross-validated AUC of 0.852 in CT- subjects and an AUC of 0.831 in a validation dataset. The optimal misclassification rate in an external dataset (n = 254) was 13%.</p><p><strong>Conclusion: </strong>If one defines concussion based on history, examination, radiographic and Sport Concussion Assessment Tool 3 criteria, it is possible to generate an eye tracking based biomarker that enables detection of concussion with reasonably high sensitivity and specificity.</p>\",\"PeriodicalId\":37006,\"journal\":{\"name\":\"Concussion\",\"volume\":\"1 1\",\"pages\":\"CNC3\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2015-08-06\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://sci-hub-pdf.com/10.2217/cnc.15.3\",\"citationCount\":\"36\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Concussion\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.2217/cnc.15.3\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"2016/3/1 0:00:00\",\"PubModel\":\"eCollection\",\"JCR\":\"Q3\",\"JCRName\":\"Medicine\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Concussion","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.2217/cnc.15.3","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2016/3/1 0:00:00","PubModel":"eCollection","JCR":"Q3","JCRName":"Medicine","Score":null,"Total":0}
Sensitivity and specificity of an eye movement tracking-based biomarker for concussion.
Object: The purpose of the current study is to determine the sensitivity and specificity of an eye tracking method as a classifier for identifying concussion.
Methods: Brain injured and control subjects prospectively underwent both eye tracking and Sport Concussion Assessment Tool 3. The results of eye tracking biomarker based classifier models were then validated against a dataset of individuals not used in building a model. The area under the curve (AUC) of receiver operating characteristics was examined.
Results: An optimal classifier based on best subset had an AUC of 0.878, and a cross-validated AUC of 0.852 in CT- subjects and an AUC of 0.831 in a validation dataset. The optimal misclassification rate in an external dataset (n = 254) was 13%.
Conclusion: If one defines concussion based on history, examination, radiographic and Sport Concussion Assessment Tool 3 criteria, it is possible to generate an eye tracking based biomarker that enables detection of concussion with reasonably high sensitivity and specificity.
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