Purpose: We are attempting to develop a navigation system for safe and effective peripancreatic lymphadenectomy in gastric cancer surgery. As a preliminary study, we examined whether or not the peripancreatic dissection line could be learned by a machine learning model (MLM).
Methods: Among the 41 patients with gastric cancer who underwent radical gastrectomy between April 2019 and January 2020, we selected 6 in whom the pancreatic contour was relatively easy to trace. The pancreatic contour was annotated by a trainer surgeon in 1242 images captured from the video recordings. The MLM was trained using the annotated images from five of the six patients. The pancreatic contour was then segmented by the trained MLM using images from the remaining patient. The same procedure was repeated for all six combinations.
Results: The median maximum intersection over union of each image was 0.708, which was higher than the threshold (0.5). However, the pancreatic contour was misidentified in parts where fatty tissue or thin vessels overlaid the pancreas in some cases.
Conclusion: The contour of the pancreas could be traced relatively well using the trained MLM. Further investigations and training of the system are needed to develop a practical navigation system.
Keywords: Artificial intelligence; Deep learning; Gastric cancer; Navigation surgery; Pancreatic fistula.
© 2022. The Author(s) under exclusive licence to Springer Nature Singapore Pte Ltd.