Applying machine learning to safe vascular anastomosis

Hiroki Umezawa, Akatsuki Kondo, Marie Taga, Rei Ogawa,
Applying machine learning to safe vascular anastomosis,
JPRAS Open,
Volume 45,
2025,
Pages 203-211,
ISSN 2352-5878,
https://doi.org/10.1016/j.jpra.2025.06.008.
(https://www.sciencedirect.com/science/article/pii/S2352587825001056)
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 in 9150 ORBEYE™ exoscope images of arterial anastomosis obtained in 2023–2024 were annotated with RectLabel pro™. These images were used to train the You Only Look Once (YOLO) model (Ultralytics) to detect the thrombus-predicting signs. The YOLO code was executed within Google Colaboratory™.
Results
After algorithm training for 100 epochs, the four objects were detected in real time, albeit with high levels of false-positive and false-negative detections.
Conclusion
Our study shows the potential of machine learning on exoscope images to generate algorithms that promote safe microsurgical anastomosis. It also shows how the recent emergence of Python code, Google Colaboratory™, and machine-learning models such as YOLO has made it possible for even programming amateurs to develop effective machine-learning algorithms. Further development of new central and graphics processing units and computational processing methods will likely lead to machine-learning applications that improve surgery and facilitate medical training.
Keywords: Microsurgery; Vascular anastomosis; Artificial intelligence; Machine-learning; Real-time processing