Evidence map›Paper›PMID 38638494›Full record

ArticleHealthcare technology letters

First-in-human real-time AI-assisted instrument deocclusion during augmented reality robotic surgery.

Jasper Hofman, Pieter De Backer, Ilaria Manghi, Jente Simoens, Ruben De Groote, Hannes Van Den Bossche, Mathieu D'Hondt, Tim Oosterlinck, Julie Lippens, Charles Van Praet and 6 more

Abstract read
In one paragraph

Article in Healthcare technology letters. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
14citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

14 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. [Artificial intelligence in urological surgery].Urologie (Heidelberg, Germany) · 2026
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

16 authors.

Jasper HofmanORSI Academy Melle Belgium.ORCID https://orcid.org/0009-0001-1867-4762
Pieter De BackerORSI Academy Melle Belgium.
Ilaria ManghiDepartment of Sciences and Methods for Engineering University of Modena and Reggio Emilia Modena Italy.
Jente SimoensORSI Academy Melle Belgium.
Ruben De GrooteORSI Academy Melle Belgium.
Hannes Van Den BosscheDepartment of Urology AZ West Hospital Veurne Belgium.
Mathieu D'HondtDepartment of Digestive and Hepatobiliary/Pancreatic Surgery AZ Groeninge Hospital Kortrijk Belgium.
Tim OosterlinckFaculty of Medicine KU Leuven Leuven Belgium.
Julie LippensFaculty of Medicine and Health Sciences, Department of Human Structure and Repair Ghent University Ghent Belgium.
Charles Van PraetDepartment of Urology Ghent University Hospital Ghent Belgium.
Federica FerragutiDepartment of Sciences and Methods for Engineering University of Modena and Reggio Emilia Modena Italy.
Charlotte DebbautIBiTech-Biommeda, Faculty of Engineering and Architecture, and CRIG Ghent University Ghent Belgium.
Zhijin LiNVIDIA Santa Clara California USA.
Oliver KutterNVIDIA Santa Clara California USA.
Alexandre MottrieORSI Academy Melle Belgium.
Karel DecaesteckerFaculty of Medicine and Health Sciences, Department of Human Structure and Repair Ghent University Ghent Belgium.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The integration of Augmented Reality (AR) into daily surgical practice is withheld by the correct registration of pre-operative data. This includes intelligent 3D model superposition whilst simultaneously handling real and virtual occlusions caused by the AR overlay. Occlusions can negatively impact surgical safety and as such deteriorate rather than improve surgical care. Robotic surgery is particularly suited to tackle these integration challenges in a stepwise approach as the robotic console allows for different inputs to be displayed in parallel to the surgeon. Nevertheless, real-time de-occlusion requires extensive computational resources which further complicates clinical integration. This work tackles the problem of instrument occlusion and presents, to the authors' best knowledge, the first-in-human on edge deployment of a real-time binary segmentation pipeline during three robot-assisted surgeries: partial nephrectomy, migrated endovascular stent removal, and liver metastasectomy. To this end, a state-of-the-art real-time segmentation and 3D model pipeline was implemented and presented to the surgeon during live surgery. The pipeline allows real-time binary segmentation of 37 non-organic surgical items, which are never occluded during AR. The application features real-time manual 3D model manipulation for correct soft tissue alignment. The proposed pipeline can contribute towards surgical safety, ergonomics, and acceptance of AR in minimally invasive surgery.

Indexed as

augmented realitycomputer visionimage processingimage segmentationlearning (artificial intelligence)medical roboticsreal‐time systemssurgery

Identifiers

PMID38638494
PMCPMC11022222

What OpenQuestion holds

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LicenceCC BY-NC-ND
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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.