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Intraoperative Detection of Surgical Gauze Using Deep Convolutional Neural Network

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Abstract

During laparoscopic surgery, surgical gauze is usually inserted into the body cavity to help achieve hemostasis. Retention of surgical gauze in the body cavity may necessitate reoperation and increase surgical risk. Using deep learning technology, this study aimed to propose a neural network model for gauze detection from the surgical video to record the presence of the gauze. The model was trained by the training group using YOLO (You Only Look Once)v5x6, then applied to the testing group. Positive predicted value (PPV), sensitivity, and mean average precision (mAP) were calculated. Furthermore, a timeline of gauze presence in the video was drawn by the model as well as human annotation to evaluate the accuracy. After the model was well-trained, the PPV, sensitivity, and mAP in the testing group were 0.920, 0.828, and 0.881, respectively. The inference time was 11.3 ms per image. The average accuracy of the model adding a marking and filtering process was 0.899. In conclusion, surgical gauze can be successfully detected using deep learning in the surgical video. Our model provided a fast detection of surgical gauze, allowing further real-time gauze tracing in laparoscopic surgery that may help surgeons recall the location of the missing gauze.

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Abbreviations

RFID:

Radio frequency identification

AI:

Artificial intelligence

CNN:

Convolutional neural network

mAP:

Mean average precision

IoU:

Intersection over Union

PPV:

Positive predictive value

ROC:

Receiver operating characteristic

PR:

Precision-recall

AP:

Average precision

FP:

False positive

TP:

True positive

TN:

True negative

CIoU:

Complete intersection over union

PET:

Positron emission tomography

fMRI:

Functional magnetic resonance imaging

CT:

Computed tomography

LBP:

Local binary patterns

RPN:

Region proposal network

GAN:

Generative adversarial network

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Acknowledgments

This work was supported by the Ministry of Science and Technology (MOST 110-2634-F-002-009) of the Republic of China (Taiwan) for the financial support in data analysis.

Conflict of interest

No benefits in any form have been or will be received from a commercial party related directly or indirectly to the subject of this manuscript.

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Authors and Affiliations

  1. Graduate Institute of Biomedical Electronics and Bioinformatics, National Taiwan University, No.1, Sec.4, Roosevelt Road, Taipei, 10617, Taiwan

    Shuo-Lun Lai, Chi-Sheng Chen & Ruey-Feng Chang

  2. Division of Colorectal Surgery, Department of Surgery, National Taiwan University Hospital and National Taiwan University College of Medicine, Taipei, Taiwan

    Shuo-Lun Lai & Been-Ren Lin

  3. Department of Computer Science and Information Engineering, National Taiwan University, Taipei, Taiwan

    Ruey-Feng Chang

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  1. Shuo-Lun Lai
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Correspondence to Ruey-Feng Chang.

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Lai, SL., Chen, CS., Lin, BR. et al. Intraoperative Detection of Surgical Gauze Using Deep Convolutional Neural Network. Ann Biomed Eng 51, 352–362 (2023). https://doi.org/10.1007/s10439-022-03033-9

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  • DOI: https://doi.org/10.1007/s10439-022-03033-9

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