Challenges in Multi-centric Generalization: Phase and Step Recognition
  in Roux-en-Y Gastric Bypass Surgery
Joel L. Lavanchy Sanat Ramesh Diego Dall'Alba Cristians Gonzalez Paolo Fiorini Beat Muller-Stich Philipp C. Nett Jacques Marescaux Didier Mutter Nicolas Padoy
Most studies on surgical activity recognition utilizing Artificial
intelligence (AI) have focused mainly on recognizing one type of activity from
small and mono-centric surgical video datasets. It remains speculative whether
those models would generalize to other centers. In this work, we introduce a
large multi-centric multi-activity dataset consisting of 140 videos
(MultiBypass140) of laparoscopic Roux-en-Y gastric bypass (LRYGB) surgeries
performed at two medical centers: the University Hospital of Strasbourg
(StrasBypass70) and Inselspital, Bern University Hospital (BernBypass70). The
dataset has been fully annotated with phases and steps. Furthermore, we assess
the generalizability and benchmark different deep learning models in 7
experimental studies: 1) Training and evaluation on BernBypass70; 2) Training
and evaluation on StrasBypass70; 3) Training and evaluation on the
MultiBypass140; 4) Training on BernBypass70, evaluation on StrasBypass70; 5)
Training on StrasBypass70, evaluation on BernBypass70; Training on
MultiBypass140, evaluation 6) on BernBypass70 and 7) on StrasBypass70. The
model's performance is markedly influenced by the training data. The worst
results were obtained in experiments 4) and 5) confirming the limited
generalization capabilities of models trained on mono-centric data. The use of
multi-centric training data, experiments 6) and 7), improves the generalization
capabilities of the models, bringing them beyond the level of independent
mono-centric training and validation (experiments 1) and 2)). MultiBypass140
shows considerable variation in surgical technique and workflow of LRYGB
procedures between centers. Therefore, generalization experiments demonstrate a
remarkable difference in model performance. These results highlight the
importance of multi-centric datasets for AI model generalization to account for
variance in surgical technique and workflows.