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@ARTICLE{Krger:305419,
      author       = {J. Kröger and N. Jorek and A. Seitel$^*$ and L. Mayer$^*$
                      and G. A. Salg and N. Crnovrsanin and F. Pianka and T.
                      Pausch and L. Maier-Hein$^*$ and C. Michalski and H.
                      Nienhüser},
      title        = {{A}pplication of artificial intelligence in esophageal
                      surgery: a systematic review.},
      journal      = {Journal of robotic surgery},
      volume       = {19},
      number       = {1},
      issn         = {1863-2483},
      address      = {London},
      publisher    = {Springer},
      reportid     = {DKFZ-2025-02148},
      pages        = {694},
      year         = {2025},
      abstract     = {The aim of this systematic review was to summarize and
                      analyze the available literature on the application of
                      artificial intelligence systems in esophageal surgery,
                      focusing on anatomy recognition, instrument detection, and
                      surgical phase recognition. Esophageal cancer poses a
                      significant global health challenge, ranking as the seventh
                      most common cancer worldwide. Esophagectomy is the only
                      curative treatment for non-metastatic esophageal cancer.
                      While the introduction of minimally invasive esophagectomy
                      and later robot-assisted minimally invasive esophagectomy
                      significantly improved surgical precision and patient
                      outcome, this development promoted a transition to
                      increasing digitalization and video processing. Subsequently
                      facilitating the integration of artificial intelligence is a
                      promising tool in the enhancement of esophageal surgery. A
                      systematic search was conducted following the PRISMA
                      guidelines in the Medline and Web of Science databases.
                      Studies published between January 2019 and June 2025
                      published in English and without restrictions to study type
                      were included. Inclusion criteria focused on artificial
                      intelligence-based anatomy recognition, instrument
                      recognition, and phase recognition in esophageal surgery.
                      Studies addressing preoperative and postoperative risk
                      prediction or artificial intelligence applications not
                      directly related to the surgical procedure were excluded.
                      The systematic literature search yielded 7063 results. After
                      screening, we identified six studies examining artificial
                      intelligence applications in esophagectomy focusing on
                      anatomy, instrument, and phase recognition. Artificial
                      intelligence can be a useful tool-especially for
                      intraoperative anatomy recognition-reaching detection rates
                      comparable to trained surgeons in real time as seen in one
                      study, reaching a Dice coefficient of 0.58, which was close
                      to that of an expert esophageal surgeon (0.62) and
                      significantly higher than the general surgeon (0.47, p=
                      0.0019). Due to the heterogeneity of study aims, utilized
                      algorithms and outcome measures direct comparison between
                      studies was not feasible. Artificial intelligence has
                      demonstrated significant potential in enhancing esophageal
                      surgery by improving anatomical recognition and optimizing
                      surgical workflow. Despite these advancements, challenges
                      remain in standardizing datasets, refinement of annotation
                      methodologies, and seamless integration into real-time
                      surgical navigation systems. To ensure clinical
                      applicability, future research should focus on large-scale
                      validation and prospective clinical trials to establish
                      artificial intelligence's clinical utility and safety in
                      minimally invasive esophagectomy.},
      subtyp        = {Review Article},
      keywords     = {Humans / Artificial Intelligence / Esophagectomy: methods /
                      Esophageal Neoplasms: surgery / Robotic Surgical Procedures:
                      methods / Esophagus: surgery / Esophagus: anatomy $\&$
                      histology / Artificial intelligence (Other) / Esophageal
                      surgery (Other) / Machine learning (Other) / Minimally
                      invasive surgery (Other)},
      cin          = {E130},
      ddc          = {610},
      cid          = {I:(DE-He78)E130-20160331},
      pnm          = {315 - Bildgebung und Radioonkologie (POF4-315)},
      pid          = {G:(DE-HGF)POF4-315},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {pmid:41099978},
      doi          = {10.1007/s11701-025-02854-9},
      url          = {https://inrepo02.dkfz.de/record/305419},
}