Evidence map›Paper›PMID 36978765›Full record

ArticleBioengineering (Basel, Switzerland)2023

Chinese Digital Arm (CDA): A High-Precision Digital Arm for Electrical Stimulation Simulation.

Shuang Zhang, Jiujiang Wang, Yuanyu Yu, Lin Wu, Tao Zhang

Open access · goldFull text read
In one paragraph

Article in Bioengineering (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
1.0field-weighted citation impact, top 27% of its field
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, 7 citations in OpenAlex.

  1. Review
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

5 authors at 2 institutions in 1 country.

Shuang ZhangThe School of Artificial Intelligence, Neijiang Normal University, Neijiang 641004, China.ORCID 0000-0002-7134-0454
Jiujiang WangThe School of Artificial Intelligence, Neijiang Normal University, Neijiang 641004, China.ORCID 0000-0001-8964-378X
Yuanyu YuThe School of Artificial Intelligence, Neijiang Normal University, Neijiang 641004, China.ORCID 0000-0003-4197-0460
Lin WuThe NJNU-OMNISKY Smart Medical Engineering Applications Joint Laboratory, Neijiang Normal University, Neijiang 641004, China.
Tao ZhangThe School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610056, China.
Neijiang Normal University · CNUniversity of Electronic Science and Technology of China · CN

Funding

the China Postdoctoral Science Founda-tion funded 2020M683294the National Key Re-search and Development Program of Chi-na 2016YFC0100800 and 2016YFC0100802the Sichuan Science and Technology Program 2022JDRC0134, 2022YFH0110, and 2021YFSC0040
6 · The paper itself

Abstract

To effectively analyze the diffusion and accumulation of signals on the surface and inside the human body under electrical stimulation, we used the gray threshold of the Chinese Digital Human image dataset to segment an arm image and reconstruct the tissue to obtain its three-dimensional cloud point dataset. Finally, a semirefined digital arm entity model with the geometric characteristics of the actual human arm tissue was constructed using reverse engineering technology. Further input of the current signal stimulation under tDCS and tACS with additional analysis of the signal diffusion in the transient mode via model calculation revealed that DC electrical stimulation is likely to cause high-voltage burns. The effective depth achieved using the AC stimulation signal is considerable, and provides reference for the electrical stimulation selection. Simultaneously, in the digital arm model, the signal diffusion and tissue damage inside the arm can be analyzed by changing the field, which provides a theoretical basis for the experimental study of the human body.

Indexed as

image generationreconstructionsegmentationstimulation

Identifiers

PMID36978765
PMCPMC10045417
OpenAlexW4327954963

What OpenQuestion holds

Textfull text, public
LicenceCC BY
measurements read22
Read underepoch 390

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.