Evidence map›Paper›PMID 39188571›Full record

ArticleFrontiers in robotics and AI2024

Adaptive approach for tracking movements of biological targets: application to robot-based intervention for prostate cancer.

Abdeslem Smahi, Othman Lakhal, Taha Chettibi, Mario Sanz Lopez, David Pasquier, Rochdi Merzouki

Abstract read
In one paragraph

Article in Frontiers in robotics and AI, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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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

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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

6 authors.

Abdeslem SmahiCRIStAL, CNRS-UMR 9189, University of Lille, Villeneuve d'Ascq, France.
Othman LakhalCRIStAL, CNRS-UMR 9189, University of Lille, Villeneuve d'Ascq, France.
Taha ChettibiDepartment of Mechanical Engineering, Blida-1 University, Blida, Algeria.
Mario Sanz LopezCRIStAL, CNRS-UMR 9189, University of Lille, Villeneuve d'Ascq, France.
David PasquierCRIStAL, CNRS-UMR 9189, University of Lille, Villeneuve d'Ascq, France.
Rochdi MerzoukiCRIStAL, CNRS-UMR 9189, University of Lille, Villeneuve d'Ascq, France.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Introduction: In this paper, we introduce an advanced robotic system integrated with an adaptive optimization algorithm, tailored for Brachytherapy in prostate cancer treatment. The primary innovation of the system is the algorithm itself, designed to dynamically adjust needle trajectories in response to the real-time movements of the prostate gland during the local intervention. Methods: The system employs real-time position data extracted from Magnetic Resonance Imaging (MRI) to ensure precise targeting of the prostate, adapting to its constant motion and deformation. This precision is crucial in Brachytherapy, where the accurate placement of radioactive seeds directly impacts the efficacy of the treatment and minimizes damage to surrounding safe tissues. Results: Our results demonstrate a marked improvement in the accuracy of radiation seed placement, directly correlating to more effective radiation delivery. The adaptive nature of the algorithm significantly reduces the number of needle insertions, leading to a less invasive treatment experience for patients. This reduction in needle insertions also contributes to lower risks of infection and shorter recovery times. Discussion: This novel robotic system, enhanced by the adaptive optimization algorithm, improves the coverage of targets reached by a traditional combinatorial approach by approximately 15% with fewer required needles. The improved precision and reduced invasiveness highlight the potential of this system to enhance the overall effectiveness and patient experience in prostate cancer Brachytherapy.

Indexed as

adaptive optimisationartificial intelligencemedical robotpath planningprediction

Identifiers

PMID39188571
PMCPMC11345532

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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.