Evidence map›Paper›PMID 40177224›Full record

ArticleFrontiers in robotics and AI2025

A flexible transoral swab sampling robot system with visual-tactile fusion approach.

Jiaxiang Dong, Peng Li, Quanquan Liu, Qi Liu, Chunbao Wang, Xuezhi Zhao, Xiping Hu

Abstract read
In one paragraph

Article in Frontiers in robotics and AI, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
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

7 authors.

Jiaxiang DongSchool of Mechanical and Automotive Engineering, South China University of Technology, Guangzhou, China.
Peng LiSchool of Mechanical and Electrical Engineering and Automation, Harbin Institute of Technology (Shenzhen), Shenzhen, China.
Quanquan LiuArtificial Intelligence Research Institute & Guangdong-Hong Kong-Macao Joint Laboratory, Shenzhen MSU-BIT University, Shenzhen, China.
Qi LiuDepartment of Neurology, The First Affiliated Hospital of Shenzhen University, Shenzhen, China.
Chunbao WangDepartment of Neurology, The First Affiliated Hospital of Shenzhen University, Shenzhen, China.
Xuezhi ZhaoSchool of Mechanical and Automotive Engineering, South China University of Technology, Guangzhou, China.
Xiping HuArtificial Intelligence Research Institute & Guangdong-Hong Kong-Macao Joint Laboratory, Shenzhen MSU-BIT University, Shenzhen, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

A significant number of individuals have been affected by pandemic diseases, such as COVID-19 and seasonal influenza. Nucleic acid testing is a common method for identifying infected patients. However, manual sampling methods require the involvement of numerous healthcare professionals. To address this challenge, we propose a novel transoral swab sampling robot designed to autonomously perform nucleic acid sampling using a visual-tactile fusion approach. The robot comprises a series-parallel hybrid flexible mechanism for precise distal posture adjustment and a visual-tactile perception module for navigation within the subject's oral cavity. The series-parallel hybrid mechanism, driven by flexible shafts, enables omnidirectional bending through coordinated movement of the two segments of the bendable joint. The visual-tactile perception module incorporates a camera to capture oral images of the subject and recognize the nucleic acid sampling point using a deep learning method. Additionally, a force sensor positioned at the distal end of the robot provides feedback on contact force as the swab is inserted into the subject's oral cavity. The sampling robot is capable of autonomously performing transoral swab sampling while navigating using the visual-tactile perception algorithm. Preliminary experimental trials indicate that the designed robot system is feasible, safe, and accurate for sample collection from subjects.

Indexed as

automatic samplingdeep learningflexible robottransoral swab manipulationvisual-tactile fusion

Identifiers

PMID40177224
PMCPMC11961991

What OpenQuestion holds

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LicenceCC BY
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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.