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Artificial Intelligence Image Recognition System for Preventing Wrong-Site Upper Limb Surgery.
Wu, Yi-Chao; Chang, Chao-Yun; Huang, Yu-Tse; Chen, Sung-Yuan; Chen, Cheng-Hsuan; Kao, Hsuan-Kai.
Afiliação
  • Wu YC; Department of Electronic Engineering, National Yunlin University of Science and Technology, Yunlin 950359, Taiwan.
  • Chang CY; Interdisciplinary Program of Green and Information Technology, National Taitung University, Taitung 950359, Taiwan.
  • Huang YT; Interdisciplinary Program of Green and Information Technology, National Taitung University, Taitung 950359, Taiwan.
  • Chen SY; Interdisciplinary Program of Green and Information Technology, National Taitung University, Taitung 950359, Taiwan.
  • Chen CH; Department of Electrical Engineering, National Central University, Taoyuan 320317, Taiwan.
  • Kao HK; Department of Electrical Engineering, Fu Jen Catholic University, New Taipei City 242062, Taiwan.
Diagnostics (Basel) ; 13(24)2023 Dec 14.
Article em En | MEDLINE | ID: mdl-38132251
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.
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Idioma: En Ano de publicação: 2023 Tipo de documento: Article

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Idioma: En Ano de publicação: 2023 Tipo de documento: Article