title: Outlier detection and removal improves accuracy of machine learning approach to multispectral burn diagnostic imaging.
abstract: Multispectral imaging (MSI) was implemented to develop a burn tissue classification device to assist burn surgeons in planning and performing debridement surgery. To build a classification model via machine learning, training data accurately representing the burn tissue was needed, but assigning raw MSI data to appropriate tissue classes is prone to error. We hypothesized that removing outliers from the training dataset would improve classification accuracy. A swine burn model was developed to build an MSI training database and study an algorithm’s burn tissue classification abilities. After the ground-truth database was generated, we developed a multistage method based on Z -test and univariate analysis to detect and remove outliers from the training dataset. Using 10-fold cross validation, we compared the algorithm’s accuracy when trained with and without the presence of outliers. The outlier detection and removal method reduced the variance of the training data. Test accuracy was improved from 63% to 76%, matching the accuracy of clinical judgment of expert burn surgeons, the current gold standard in burn injury assessment. Given that there are few surgeons and facilities specializing in burn care, this technology may improve the standard of burn care for patients without access to specialized facilities.
authors: Spectral MD, Inc., 2515 McKinney Avenue, Suite 1000, Dallas, Texas 75201, United States.
authors: Weizhi
authors: W
authors: Li
authors: Spectral MD, Inc., 2515 McKinney Avenue, Suite 1000, Dallas, Texas 75201, United States.
authors: Weirong
authors: W
authors: Mo
authors: Spectral MD, Inc., 2515 McKinney Avenue, Suite 1000, Dallas, Texas 75201, United States.
authors: Xu
authors: X
authors: Zhang
authors: Spectral MD, Inc., 2515 McKinney Avenue, Suite 1000, Dallas, Texas 75201, United StatesbBaylor Research Institute, 3310 Live Oak, Suite 501, Dallas, Texas 75204, United States.
authors: John J
authors: JJ
authors: Squiers
authors: Spectral MD, Inc., 2515 McKinney Avenue, Suite 1000, Dallas, Texas 75201, United States.
authors: Yang
authors: Y
authors: Lu
authors: Spectral MD, Inc., 2515 McKinney Avenue, Suite 1000, Dallas, Texas 75201, United States.
authors: Eric W
authors: EW
authors: Sellke
authors: Spectral MD, Inc., 2515 McKinney Avenue, Suite 1000, Dallas, Texas 75201, United States.
authors: Wensheng
authors: W
authors: Fan
authors: Spectral MD, Inc., 2515 McKinney Avenue, Suite 1000, Dallas, Texas 75201, United StatesbBaylor Research Institute, 3310 Live Oak, Suite 501, Dallas, Texas 75204, United States.
authors: J Michael
authors: JM
authors: DiMaio
authors: Spectral MD, Inc., 2515 McKinney Avenue, Suite 1000, Dallas, Texas 75201, United States.
authors: Jeffrey E
authors: JE
authors: Thatcher
mesh_terms: D000465:Algorithms; D000818:Animals; D016477:Artifacts; D002056:Burns / Q000473:pathology*; D046169:Dermoscopy / Q000379:methods; D007089:Image Enhancement / Q000379:methods*; D007090:Image Interpretation, Computer-Assisted / Q000379:methods; D000069550:Machine Learning; D061848:Optical Imaging / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D012867:Skin / Q000293:injuries / Q000473:pathology; D013057:Spectrum Analysis / Q000379:methods*; D013382:Subtraction Technique; D013552:Swine