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Related Experiment Video

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

Yi-Chao Wu1, Chao-Yun Chang2, Yu-Tse Huang2

  • 1Department of Electronic Engineering, National Yunlin University of Science and Technology, Yunlin 950359, Taiwan.

Diagnostics (Basel, Switzerland)
|December 22, 2023
PubMed
Summary
This summary is machine-generated.

An AI image recognition system accurately distinguishes left and right upper limbs, preventing wrong-site surgeries. This Artificial Intelligence Image Recognition System (AIIRS) achieved high precision and recall rates in preventing surgical errors.

Keywords:
IRBaccuracy rateintelligent image recognitionrecall ratewrong-site left and right upper limb surgery

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Area of Science:

  • Orthopedic Surgery
  • Medical Imaging
  • Artificial Intelligence

Background:

  • Wrong-site surgery remains a critical patient safety concern in orthopedic procedures.
  • Accurate identification of left and right upper limbs is essential for preventing surgical errors.

Purpose of the Study:

  • To develop and evaluate an Artificial Intelligence Image Recognition System (AIIRS) for differentiating left and right upper limbs.
  • To assess the efficacy of AIIRS in preventing wrong-site upper limb surgery.

Main Methods:

  • Implementation of a deep learning model for image recognition.
  • Training and testing the AIIRS on a dataset of upper limb images.
  • Experimental validation of the system's precision and recall rates.

Main Results:

  • The AIIRS achieved a precision rate of 98% and a recall rate of 93% in differentiating upper limbs.
  • Experimental results demonstrated the system's capability to assist in preventing wrong-site surgery.

Conclusions:

  • The developed AIIRS shows significant potential in enhancing patient safety in orthopedic surgery.
  • Further human trials are planned to validate the system's clinical utility.
  • This technology offers substantial benefits and research value for upper limb orthopedic procedures.