Julie Blaszczak, Sara Wiener, Melissa Plegue, Dan Shumer, John Shatzer, Anita Hernandez
Evaluating the effectiveness of an online curriculum on caring for transgender and nonbinary patients
Medical education online
hyperspectral imaging hsi combined machine learning help identify characteristic tissue signatures enabling automatic tissue recognition surgery study aims develop first hsibased automatic abdominal tissue recognition human data prospective bicenter setting data collected patients undergoing elective open abdominal surgery two international tertiary referral hospitals september 2020 june 2021 hs images captured various time points throughout surgical procedure resulting rgb images annotated 13 distinct organ labels convolutional neural networks cnns employed analysis external internal validation settings utilized total 169 patients included 73 432 strasbourg 96 568 verona internal validation within centers combined patients centers single cohort randomly allocated training 127 patients 751 585 images test sets 42 patients 249 181 images validation setting showed best performance highest true positive rate achieved skin 100 liver 97 misclassifications included tissues similar embryological origin omentum mesentery 32 overlaying boundaries liver hepatic ligament 22 median dice score ten tissue classes exceeded 80 improve automatic surgical scene segmentation drive clinical translation multicenter accurate hsi datasets essential work needed quantify clinical value hsi hsi might included new omics science namely surgical optomics uses light extract quantifiable tissue features surgery