Indocyanine green quantification in full robotic esophagectomy using an unsupervised learning approach
An unsupervised learning approach to quantify intraoperative indocyanine green imaging could aid the prediction of anastomotic leakage after robotic-assisted minimally invasive esophagectomy in future treatments. However, the value of this approach needs to be clarified in a randomized, controlled prospective study.
Authors
- Andreas Pascher (ORCID: https://orcid.org/0000-0002-0801-1949)
- Nicola Sariye Pollmann (ORCID: https://orcid.org/0000-0002-6758-312X)
- Jens Peter Hölzen (ORCID: https://orcid.org/0000-0003-1720-8943)
- Sebastian Weberskirch
- Mazen A. Juratli (ORCID: https://orcid.org/0000-0002-1793-0672)
- Eike Bormann
- Marcelo R. Petry (ORCID: https://orcid.org/0000-0002-7023-8562)
- Lukas Pollmann (ORCID: https://orcid.org/0000-0002-8810-7106)
- Sebastian Kubasch (ORCID: https://orcid.org/0009-0005-3140-0722)
Institutions
- Twitter (United States) (US)
- Augenstern (DE)
- University Hospital Münster (DE)
Publication Details
- Journal
- Surgery
- Published
- 2025-05-23
- DOI
- https://doi.org/10.1016/j.surg.2025.109405
- Citations
- 2
- Primary Topic
- Esophageal Cancer Research and Treatment
- Type
- article
- Field-Weighted Citation Impact
- 4.01