Expert surgeons and deep learning models can predict the outcome of surgical hemorrhage from 1 min of video.

ABSTRACT: Major vascular injury resulting in uncontrolled bleeding is a catastrophic and often fatal complication of minimally invasive surgery. At the outset of these events, surgeons do not know how much blood will be lost or whether they will successfully control the hemorrhage (achieve hemostasis). We evaluate the ability of a deep learning neural network (DNN) to predict hemostasis control ability using the first minute of surgical video and compare model performance with human experts viewing the same video. The publicly available SOCAL dataset contains 147 videos of attending and resident surgeons managing hemorrhage in a validated, high-fidelity cadaveric simulator. Videos are labeled with outcome and blood loss (mL). The first minute of 20 videos was shown to four, blinded, fellowship train

SUBMITTER: Pangal DJ 

PROVIDER: S-EPMC9114003 | biostudies-literature | 2022 May

REPOSITORIES: biostudies-literature