Evidence map›Paper›PMID 42533186›Full record

ArticleInternational journal of computer assisted radiology and surgery2026

Robotic ultrasound scanning platform with autonomous control, multimodal human-machine interface and real-time image analysis.

Rafael Benito, Laura Pérez Sánchez, Amaia Iribar-Zabala, Marco Ojer, Marcos Garro, Verónica de Ramos, Jon Ortega, Álvaro Bertelsen, Xiao Lin, Ignacio Sánchez-Varo and 7 more

Abstract read
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In one paragraph

Article in International journal of computer assisted radiology and surgery, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

17 authors.

Rafael BenitoVicomtech Foundation, Basque Research and Technology Alliance (BRTA), San Sebastian, Spain. rafael.benito01@estudiant.upf.edu.ORCID http://orcid.org/0000-0001-9866-0864
Laura Pérez SánchezVicomtech Foundation, Basque Research and Technology Alliance (BRTA), San Sebastian, Spain.
Amaia Iribar-ZabalaVicomtech Foundation, Basque Research and Technology Alliance (BRTA), San Sebastian, Spain.
Marco OjerVicomtech Foundation, Basque Research and Technology Alliance (BRTA), San Sebastian, Spain.
Marcos GarroVicomtech Foundation, Basque Research and Technology Alliance (BRTA), San Sebastian, Spain.
Verónica de RamosVicomtech Foundation, Basque Research and Technology Alliance (BRTA), San Sebastian, Spain.
Jon OrtegaVicomtech Foundation, Basque Research and Technology Alliance (BRTA), San Sebastian, Spain.
Álvaro BertelsenVicomtech Foundation, Basque Research and Technology Alliance (BRTA), San Sebastian, Spain.
Xiao LinVicomtech Foundation, Basque Research and Technology Alliance (BRTA), San Sebastian, Spain.
Ignacio Sánchez-VaroBioengineering and Health Technologies Unit, Jesús Usón Minimally Invasive Surgery Centre, Cáceres, Spain.
Lucía SalazarBioengineering and Health Technologies Unit, Jesús Usón Minimally Invasive Surgery Centre, Cáceres, Spain.
Juan Alberto Sánchez-MargalloBioengineering and Health Technologies Unit, Jesús Usón Minimally Invasive Surgery Centre, Cáceres, Spain.
Mikael BrudforsNVIDIA, Santa Clara, CA, USA.
Nadim DaherNVIDIA, Santa Clara, CA, USA.
Karen López-LinaresVicomtech Foundation, Basque Research and Technology Alliance (BRTA), San Sebastian, Spain.
Davide ScorzaCyber Surgery, San Sebastian, Spain.
Miguel Ángel González BallesterBCN Medtech, Universitat Pompeu Fabra, Barcelona, Spain.

Funding

Eusko Jaurlaritza KK-2025/00028
6 · The paper itself

Abstract

purposeAutonomous robots can streamline repetitive and time-consuming surgical tasks like ultrasound scanning. AI can provide the necessary intelligence, but for clinical acceptance, systems must move predictably, offer intuitive interaction, and maintain low latency on medical-grade hardware.

methodsWe present an integral robotic platform for autonomous ultrasound scanning. The platform allows to perform scanning on either manual or autonomous mode. The autonomous control combines AI-driven ultrasound target segmentation with geometric motion algorithms designed to focus the probe on the segmented target. We optimized the image processing models for specialized hardware to ensure real-time performance with constrained resources. Users can control the platform via natural language voice commands processed by a Large Language Model (LLM) and visualize the procedure through a synchronized 3D Digital Twin and an augmented reality (AR) environment.

resultsWe tested the platform in hepatic tumor localization in a synthetic phantom. Autonomous tumor localization showed a success rate of 84% with an average execution time of 5.75 s.

conclusionsOur system successfully automates ultrasound tasks, allowing the user to control the process through an intuitive, multimodal interface. By pairing optimized AI with specialized hardware, we achieved low-latency, real-time adaptability with minimal hardware resources. In the future we plan to adapt this platform for more complex neurosurgical and urological procedures.

Indexed as

Autonomous roboticsSurgical navigationSurgical roboticsUltrasound scanning

Identifiers

PMID42533186

What OpenQuestion holds

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Registered trials

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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.