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Surgical Instrument Detection Algorithm Based on Improved YOLOv7x.

Boping Ran1, Bo Huang1, Shunpan Liang1

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|June 10, 2023
PubMed
Summary
This summary is machine-generated.

This study enhances YOLOv7x for accurate surgical instrument counting using computer vision, improving patient safety. The improved algorithm shows superior performance in detecting instruments, even with challenging visual conditions.

Keywords:
YOLOV7xcomputer visiondeep learningsurgical instrument detection

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Area of Science:

  • Computer Vision
  • Medical Imaging
  • Artificial Intelligence in Healthcare

Background:

  • Manual surgical instrument counting poses risks of errors, impacting patient safety and leading to medical disputes.
  • Existing computer vision methods struggle with densely packed, occluded, or similarly shaped surgical instruments under varied lighting.
  • Advancements in medical informatization require robust automated solutions for surgical instrument management.

Purpose of the Study:

  • To improve the accuracy and robustness of surgical instrument detection using an enhanced object detection algorithm.
  • To address challenges like instrument occlusion, dense arrangement, similar appearances, and varying lighting conditions.
  • To contribute to safer surgical procedures and the development of medical informatization.

Main Methods:

  • Modified the YOLOv7x object detection algorithm by incorporating the RepLK Block module into the backbone network to enhance shape feature learning.
  • Integrated the ODConv structure into the neck module to improve feature extraction capabilities and contextual information capture.
  • Developed and utilized the OSI26 dataset, comprising 452 images of 26 surgical instruments, for model training and validation.

Main Results:

  • The improved YOLOv7x algorithm achieved high performance metrics: F1 score (94.7%), AP (91.5%), AP50 (99.1%), and AP75 (98.2%).
  • Demonstrated significant improvements over the baseline algorithm, with increases of 4.6%, 3.1%, 3.6%, and 3.9% in respective metrics.
  • Outperformed other mainstream object detection algorithms, indicating superior accuracy and robustness in surgical instrument detection.

Conclusions:

  • The enhanced YOLOv7x algorithm effectively addresses challenges in surgical instrument detection, offering higher accuracy and robustness.
  • The proposed method significantly improves the identification of surgical instruments, contributing to enhanced surgical safety and patient health.
  • This advancement supports the broader goals of medical informatization through reliable automated instrument management.