TY  - JOUR
AU  - Brandenburg, Johanna
AU  - Schulze, André
AU  - Jenke, Alexander
AU  - Bhasker, Nithya
AU  - Bleser, Noelle
AU  - Junger, Denise
AU  - Stern, Antonia
AU  - Rivoir, Dominik
AU  - Naderi, Hamid
AU  - Fritz-Kebede, Fleur
AU  - Burgert, Oliver
AU  - Maier-Hein, Lena
AU  - Mündermann, Lars
AU  - Bodenstedt, Sebastian
AU  - Speidel, Stefanie
AU  - Lozanovski, Vladimir J.
AU  - Grimminger, Peter P.
AU  - Billeter, Adrian
AU  - Klotz, Rosa
AU  - Weitz, Jürgen
AU  - Distler, Marius
AU  - Müller-Stich, Beat P.
AU  - Wagner, Martin
TI  - Surgical workflow analysis for Surgomics and context-aware assistance in robot-assisted minimally invasive esophagectomy (RAMIE): a retrospective, single-arm, multicenter annotation and machine learning study
JO  - European journal of surgical oncology
VL  - 52
IS  - 1
SN  - 0748-7983
CY  - Burlington, Mass.
PB  - Harcourt
M1  - DKFZ-2025-02695
SP  - 111174 - 111184
PY  - 2026
AB  - IntroductionRobot-assisted minimally invasive esophagectomy (RAMIE) is a complex procedure that may benefit from workflow analysis for context-aware assistance and surgical data science. This study aimed to model the RAMIE workflow, validate the applicability of the obtained workflow model in the operating room (OR) and retrospectively assess its generalizability across three academic centers using video data and automated workflow analysis with machine learning (ML).MethodsA RAMIE workflow model was developed based on currently available literature, participatory OR observation, and expert interviews. This model was formalized to be included into a checklist tool to document the workflow live in the OR. To investigate generalizability of the workflow model, the surgical phases of 36 RAMIE videos from three different academic hospitals were retrospectively annotated. Based on this data set, a ML model was trained and tested within a six-fold cross validation.ResultsTen surgical phases with 60 underlying steps were identified for RAMIE. The applicability of the workflow model was validated with live documentation in the OR. Multicenter video annotations revealed significant inter-institutional differences in the duration of all ten RAMIE phases. The ML model for automatic phase recognition showed an accuracy of 0.872 ± 0.091 and an f1-score of 0.872 ± 0.082 over all videos. The center with the best performing videos achieved a mean accuracy of 0.919 ± 0.036.ConclusionThe RAMIE workflow was successfully modeled and validated in a retrospective multicenter setting. Despite high variability in phase duration between surgical centers, ML-based phase recognition achieved highly promising results.
LB  - PUB:(DE-HGF)16
DO  - DOI:10.1016/j.ejso.2025.111174
UR  - https://inrepo02.dkfz.de/record/306705
ER  -