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<p>Gauze sponges are the items most commonly retained from surgery. The additional time required to find the missing gauze sponge increases anesthetic risk and causes a delay for the next surgery. In minimally invasive surgery, a digital camera can record any object on the screen during surgical procedure. This study aimed to compare modern object detection methods and propose a gauze tracking model to detect and trace the location of gauze sponges in surgical videos. The model consisted of a detection module and a regulating module. The methods used in the detection module included the YOLO series and faster R-CNN with different backbones. The regulating module was designed to reduce false positive detections. The model detected gauze and converted an entire video into a timeline to illustrate segments when gauze appeared on the screen. The timeline was compared frame-by-frame with human annotations. Faster R-CNN, with ResNet101-FPN as the backbone, outperformed other methods. Adding a regulating module further increased the accuracy and F1-score to 0.94 and 0.862, respectively. The model was trained and tested using human surgical videos. The presence of gauze sponge identified by the model was consistent with human annotations. The results are promising for the possibility of real-time gauze tracking during surgery. The model is able to provide critical information to help surgeons locate missing gauze sponges. </p> Graphical Abstract <p></p>

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Detection and tracking of a gauze sponge in minimally invasive surgery using a YOLO and R-CNN based model

  • 期刊论文

  • 发表日期：

    2025年11月19日

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  • Shuo-Lun Lai,
  • Yung-Chien Chou,
  • Chi-Sheng Chen,
  • Tzu-Chia Tung,
  • Been-Ren Lin,
  • Ruey-Feng Chang

摘要

Gauze sponges are the items most commonly retained from surgery. The additional time required to find the missing gauze sponge increases anesthetic risk and causes a delay for the next surgery. In minimally invasive surgery, a digital camera can record any object on the screen during surgical procedure. This study aimed to compare modern object detection methods and propose a gauze tracking model to detect and trace the location of gauze sponges in surgical videos. The model consisted of a detection module and a regulating module. The methods used in the detection module included the YOLO series and faster R-CNN with different backbones. The regulating module was designed to reduce false positive detections. The model detected gauze and converted an entire video into a timeline to illustrate segments when gauze appeared on the screen. The timeline was compared frame-by-frame with human annotations. Faster R-CNN, with ResNet101-FPN as the backbone, outperformed other methods. Adding a regulating module further increased the accuracy and F1-score to 0.94 and 0.862, respectively. The model was trained and tested using human surgical videos. The presence of gauze sponge identified by the model was consistent with human annotations. The results are promising for the possibility of real-time gauze tracking during surgery. The model is able to provide critical information to help surgeons locate missing gauze sponges.

Graphical Abstract