Evidence map›Paper›PMID 38132251›Full record

ArticleDiagnostics (Basel, Switzerland)2023

Artificial Intelligence Image Recognition System for Preventing Wrong-Site Upper Limb Surgery.

Yi-Chao Wu, Chao-Yun Chang, Yu-Tse Huang, Sung-Yuan Chen, Cheng-Hsuan Chen, Hsuan-Kai Kao

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Diagnostics (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Review
  2. 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

6 authors.

Yi-Chao WuDepartment of Electronic Engineering, National Yunlin University of Science and Technology, Yunlin 950359, Taiwan.ORCID 0009-0005-6665-583X
Chao-Yun ChangInterdisciplinary Program of Green and Information Technology, National Taitung University, Taitung 950359, Taiwan.
Yu-Tse HuangInterdisciplinary Program of Green and Information Technology, National Taitung University, Taitung 950359, Taiwan.
Sung-Yuan ChenInterdisciplinary Program of Green and Information Technology, National Taitung University, Taitung 950359, Taiwan.
Cheng-Hsuan ChenDepartment of Electrical Engineering, National Central University, Taoyuan 320317, Taiwan.ORCID 0000-0002-4270-1513
Hsuan-Kai KaoDepartment of Orthopedic Surgery, Chang Gung Memorial Hospital at Linkou, Taoyuan 333423, Taiwan.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Our image recognition system employs a deep learning model to differentiate between the left and right upper limbs in images, allowing doctors to determine the correct surgical position. From the experimental results, it was found that the precision rate and the recall rate of the intelligent image recognition system for preventing wrong-site upper limb surgery proposed in this paper could reach 98% and 93%, respectively. The results proved that our Artificial Intelligence Image Recognition System (AIIRS) could indeed assist orthopedic surgeons in preventing the occurrence of wrong-site left and right upper limb surgery. At the same time, in future, we will apply for an IRB based on our prototype experimental results and we will conduct the second phase of human trials. The results of this research paper are of great benefit and research value to upper limb orthopedic surgery.

Indexed as

accuracy rateintelligent image recognitionIRBrecall ratewrong-site left and right upper limb surgery

Identifiers

PMID38132251
PMCPMC10743305

What Socratic holds

Textmetadata
LicenceCC BY
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 Socratic graph.