Evidence map›Paper›PMID 42423778›Full record

ArticleSurgical endoscopy2026

Automatic recognition of anatomical structures and surgical phases in robot-assisted minimally invasive esophagectomy (RAMIE) using deep learning: a retrospective cohort study.

Romy C van Jaarsveld, Yiping Li, Ronald L P D de Jong, Franco Badaloni, Gino M Kuiper, Tim J M Jaspers, Marcel Breeuwer, Fons van der Sommen, Richard van Hillegersberg, Yasmina Al Khalil and 1 more

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Article in Surgical endoscopy, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

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1citing papers in PubMed
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1 · What the graph read from it

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

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3 · Its place in the literature

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1 citing paper in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

11 authors.

Romy C van Jaarsveld *Department of Surgery, University Medical Center Utrecht, Heidelberglaan 100, 3584 CX, Utrecht, The Netherlands. r.c.vanjaarsveld-3@umcutrecht.nl.ORCID http://orcid.org/0000-0002-0025-9261
Yiping Li *Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands.
Ronald L P D de Jong *Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands.
Franco BadaloniDepartment of Surgery, University Medical Center Utrecht, Heidelberglaan 100, 3584 CX, Utrecht, The Netherlands.
Gino M KuiperDepartment of Surgery, University Medical Center Utrecht, Heidelberglaan 100, 3584 CX, Utrecht, The Netherlands.
Tim J M JaspersDepartment of Electrical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands.
Marcel BreeuwerDepartment of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands.
Fons van der SommenDepartment of Electrical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands.
Richard van HillegersbergDepartment of Surgery, University Medical Center Utrecht, Heidelberglaan 100, 3584 CX, Utrecht, The Netherlands.
Yasmina Al KhalilDepartment of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands.
Jelle P RuurdaDepartment of Surgery, University Medical Center Utrecht, Heidelberglaan 100, 3584 CX, Utrecht, The Netherlands.

Funding

Hanarth Fonds 2022-13Nederlandse Organisatie voor Wetenschappelijk Onderzoek KICH1.ST03.21.019Nederlandse Organisatie voor Wetenschappelijk Onderzoek OCENW.M.21.377
6 · The paper itself

Abstract

backgroundCurative treatment of resectable esophageal cancer comprises neoadjuvant chemoradiotherapy and esophagectomy. Robot-assisted minimally invasive esophagectomy (RAMIE) is the preferred technique; however, learning RAMIE is challenging due to the complex chest anatomy, patient positioning and zoomed-in camera view. Computer-aided anatomy recognition holds promise for improving surgical navigation. This study aims to develop real-time anatomy recognition and surgical phase recognition algorithms for the thoracic part of RAMIE using deep learning and to understand the challenges of current state-of-the-art algorithms.

methodsA retrospective single-center cohort study was conducted on prospectively collected RAMIE videos at University Medical Center Utrecht, The Netherlands. Two datasets were created: an anatomy segmentation dataset with 1504 frames from 53 videos, annotated for eight anatomical classes and four surgical instruments; and a surgical phase dataset, with 38 videos labeled with thirteen distinct phases. Several deep learning models were trained and tested using both datasets.

resultsThe SegNeXt model achieved the most accurate segmentations with an overall overlap score of 0.72 for all classes, of which the surgical instruments were best detectable. The best surgical phase recognition model achieved an overall accuracy of 82.8% and revealed loss of accuracy during phase transitions. Class imbalance affected both datasets which led to less frequent appearing classes performing lowest.

conclusionsThis study identified suitable models for computer-aided, real-time anatomy and surgical tool segmentation and surgical phase recognition for the thoracic part of RAMIE. Acceptable performance was achieved while both datasets are highly complex. Performance is expected to improve by further expanding and diversifying the datasets.

Indexed as

Deep LearningEsophageal NeoplasmsEsophagectomyRobotic Surgical ProceduresAlgorithmsHumansMinimally Invasive Surgical ProceduresRetrospective StudiesAnatomy segmentationArtificial intelligenceDeep learningRobotic esophagectomySurgical computer visionSurgical phase recognition

Identifiers

PMID42423778
PMCPMC13582270

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