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

doi:10.3390/diagnostics13243667

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^*

^*Corresponding author for this work

School of Medicine

Research output: Contribution to journal › Journal Article › peer-review

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.

Original language	English
Article number	3667
Journal	Diagnostics
Volume	13
Issue number	24
DOIs	https://doi.org/10.3390/diagnostics13243667
State	Published - 12 2023

Bibliographical note

Publisher Copyright:
© 2023 by the authors.

Keywords

IRB
accuracy rate
intelligent image recognition
recall rate
wrong-site left and right upper limb surgery

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10.3390/diagnostics13243667

Cite this

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title = "Artificial Intelligence Image Recognition System for Preventing Wrong-Site Upper Limb Surgery",

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.",

keywords = "IRB, accuracy rate, intelligent image recognition, recall rate, wrong-site left and right upper limb surgery",

author = "Wu, {Yi Chao} and Chang, {Chao Yun} and Huang, {Yu Tse} and Chen, {Sung Yuan} and Chen, {Cheng Hsuan} and Kao, {Hsuan Kai}",

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doi = "10.3390/diagnostics13243667",

language = "英语",

volume = "13",

journal = "Diagnostics",

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AU - Wu, Yi Chao

AU - Chang, Chao Yun

AU - Huang, Yu Tse

AU - Chen, Sung Yuan

AU - Chen, Cheng Hsuan

AU - Kao, Hsuan Kai

PY - 2023/12

Y1 - 2023/12

N2 - 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.

AB - 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.

KW - IRB

KW - accuracy rate

KW - intelligent image recognition

KW - recall rate

KW - wrong-site left and right upper limb surgery

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

Abstract

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Keywords

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