Shachar Snir, Yupeng Chen, Matan Yechezkel, Tal Patalon, Erez Shmueli, Margaret L Brandeau, Dan Yamin
Changes in behavior and biomarkers during the diagnostic decision period for COVID-19, influenza, and group A streptococcus (GAS): a two-year prospective cohort study in Israel
The Lancet regional health. Europe
evaluation skin recovery important step treatment burns however conventional methods observe surface skin cannot quantify injury volume optical coherence tomography oct noninvasive noncontact realtime technique swept source oct uses near infrared light analyzes intensity light echo different depths generate images optical interference signals quantify dynamic recovery skin burns time laser induced skin burns mice evaluated using deep learning swept source oct images laserinduced mouse skin thermal injury model established thirty kunming mice oct images normal burned areas mouse skin acquired day 0 day 1 day 3 day 7 day 14 laser irradiation resulted 7000 normal 1400 burn bscan images divided training validation test sets 81505 ratio normal data 811 burn data normal images manually annotated deep learning unet model verified pspne hrnet models used segment skin three layers dermal epidermal layer subcutaneous fat layer muscle layer burn images models trained segment damaged area threedimensional reconstruction technology used reconstruct damaged tissue calculate damaged tissue volume average iou value fscore normal tissue layer unet segmentation model 0876 0934 respectively iou value burn area segmentation model reached 0907 fscore value reached 0951 compared manual labeling unet model faster higher accuracy skin stratification oct unet segmentation provide rapid accurate analysis tissue changes clinical guidance treatment burns