Kullaya Takkavatakarn, Wonsuk Oh, Lili Chan, Ira Hofer, Khaled Shawwa, Monica Kraft, Neomi Shah, Roopa Kohli-Seth, Girish N Nadkarni, Ankit Sakhuja
Machine learning derived serum creatinine trajectories in acute kidney injury in critically ill patients with sepsis
Critical care (London, England)
retrospective study analyzed errors generated convolutional neural network cnn performing automated classification oral lesions according clinical characteristics seeking identify patterns systemic errors intermediate layers cnn crosssectional analysis nested previous trial automated classification cnn model elementary lesions clinical images oral lesions performed resulting cnn classification errors formed dataset study total 116 real outputs identified diverged estimated outputs representing 76 total images analyzed cnn discrepancies real estimated outputs associated problems relating image sharpness resolution focus human errors impact data augmentation qualitative analysis errors process automated classification clinical images possible confirm impact image quality well identify strong impact data augmentation process knowledge factors models evaluate make decisions increase confidence high classification potential cnns