Application of artificial intelligence in esophageal surgery: a systematic review - Kröger, Janosch; Jorek, Nicolas; Seitel, Alexander; Mayer, Leon; Salg, Gabriel Alexander; Crnovrsanin, Nerma; Pianka, Frank; Pausch, Thomas; Maier-Hein, Lena; Michalski, Christoph; Nienhüser, Henrik (Journal of robotic surgery, 2025)
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.
Gesehen am 10.02.2026