Indocyanine green quantification in full robotic esophagectomy using an unsupervised learning approach.
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| Title: | Indocyanine green quantification in full robotic esophagectomy using an unsupervised learning approach. |
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| Authors: | Pollmann L; Department of General, Visceral and Transplant Surgery, University Hospital Muenster, Muenster, Germany., Weberskirch S; Department of General, Visceral and Transplant Surgery, University Hospital Muenster, Muenster, Germany., Petry M; ITK Engineering AG, Rülzheim, Germany., Kubasch S; Department of Clinical Radiology, University Hospital Muenster, Muenster, Germany., Pollmann NS; Department of General, Visceral and Transplant Surgery, University Hospital Muenster, Muenster, Germany., Bormann E; Institute of Biometry and Clinical Research, University Muenster, Muenster, Germany., Pascher A; Department of General, Visceral and Transplant Surgery, University Hospital Muenster, Muenster, Germany., Juratli M; Department of General, Visceral and Transplant Surgery, University Hospital Muenster, Muenster, Germany. Electronic address: https://twitter.com/mjuratli., Hölzen JP; Department of General, Visceral and Transplant Surgery, University Hospital Muenster, Muenster, Germany. Electronic address: Jenspeter.Hoelzen@ukmuenster.de. |
| Source: | Surgery [Surgery] 2025 Aug; Vol. 184, pp. 109405. Date of Electronic Publication: 2025 May 23. |
| Publication Type: | Journal Article |
| Journal Info: | Publisher: Mosby Country of Publication: United States NLM ID: 0417347 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1532-7361 (Electronic) Linking ISSN: 00396060 NLM ISO Abbreviation: Surgery Subsets: MEDLINE |
| Database: | MEDLINE Ultimate |
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