E Hartmann, K E Bøe, G H M Jørgensen, C M Mejdell, K Dahlborn
Management of horses with focus on blanketing and clipping practices reported by members of the Swedish and Norwegian equestrian community
Journal of animal science
introduction chemical composition analysis important prevention counseling kidney stone disease advances laser technology made dusting techniques prevalent offers consistent way collect enough material send chemical analysis leading many forgo test developed novel machine learning ml model effectively assess stone composition based intraoperative endoscopic video data methods two endourologists performed ureteroscopy kidney stones 10 mm representative videos recorded intraoperatively individual frames extracted videos stone outlined human tracing ml model urosam built trained automatically identify kidney stones images predict majority stone composition follows calcium oxalate monohydrate com dihydrate cod calcium phosphate cap uric acid ua urosam built top publicly available segment anything model sam incorporated unet convolutional neural network cnn discussion total 78 ureteroscopy videos collected 50 used model exclusions 32 com 8 cod 8 cap 2 ua ml model segmented images 9477 precision dice coefficient 09135 intersection union 08496 confirmed good segmentation performance ml model videowise evaluation demonstrated 60 correct classification stone composition subgroup analysis showed correct classification 844 com videos post hoc adaptive threshold technique used mitigate biasing model toward com data imbalance improved overall correct classification 62 improving classification cod cap ua videos conclusions study demonstrates effective development urosam ml model precisely identifies kidney stones natural endoscopic video data highquality video data improve performance model classifying majority stone composition