Applying machine learning to safe vascular anastomosis.

ABSTRACT:

Background

Machine-learning technology is currently being introduced into the medical field and has been shown to aid diagnostic imaging, patient examinations, patient-data analysis, various surgical aspects, and medical education. Recent advances in exoscopes and monitors are prompting a shift from optical microscope-based microsurgery to heads-up microsurgery. The high-definition exoscope images are highly suitable for machine learning. Since an algorithm that detects predictive signs of thrombus formation would aid microsurgery and help train surgeons to identify vessels at risk of unsafe microvascular anastomosis, we here asked whether we could use exoscope images to train such a machine-learning algorithm.

Methods

Arterial clots, intimal-wall damage, debris, and stumps

SUBMITTER: Umezawa H 

PROVIDER: S-EPMC12269626 | biostudies-literature | 2025 Sep

REPOSITORIES: biostudies-literature