Scholarlink

Background <p>Accurate tracking and enumeration of surgical instruments are critical for patient safety and operational efficiency in laparoscopic procedures. Advanced tracking systems enhance object detection by maintaining instrument identity despite rapid movements, overlaps, or occlusions, ensuring real-time precision and preventing misplacement.</p> Objective <p>This research aims to improve real-time detection and tracking of surgical instruments in minimally invasive surgery (MIS) by integrating a sophisticated deep learning (DL) framework. Utilizing cutting-edge computer vision (CV) techniques, the proposed approach enhances surgical workflows, minimizes errors, and improves patient safety with increased precision and efficiency.</p> Methods <p>The framework incorporates YOLOv9n, the latest iteration released in 2024, along with advanced tracking algorithms such as ByteTrack and BoT-SORT to enable real-time detection, tracking, and enumeration of surgical instruments. The YOLOv9n model underwent evaluation against YOLOv8n, YOLOv5n, YOLOv11n, and Faster R-CNN to ensure an optimal balance of speed and accuracy. The study employed the m2cai16-tool-locations detection dataset, utilizing advanced preprocessing and data augmentation techniques to enhance model performance and mitigate dataset imbalances.</p> Results <p>Comparative assessments demonstrated that YOLOv9n, when combined with ByteTrack and BoT-SORT, achieved outstanding performance with a Mean Average Precision (mAP50) of 98.4% and an inference speed of 0.3 milliseconds. This system ensures robust tracking even under rapid movements and partial occlusions, significantly improving precision and operational efficiency in MIS.</p> Conclusion <p>This automated deep learning solution reduces the cognitive burden on surgical teams, enhances patient safety, and optimizes procedural efficiency. Its potential integration with IoT, mobile applications, and intelligent operating rooms underscores its transformative role in modern surgery, setting new standards for AI-driven laparoscopic procedures and surgical innovation.</p> Graphic abstract <p></p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Enhanced deep learning framework for real-time instrument detection and tracking in laparoscopic surgery using advanced augmentation and tracking techniques

  • 期刊论文

  • 发表日期：

    2025年6月30日

查看全文
  • Priyanka Anup Ujjainkar,
  • Shital A. Raut

摘要

Background

Accurate tracking and enumeration of surgical instruments are critical for patient safety and operational efficiency in laparoscopic procedures. Advanced tracking systems enhance object detection by maintaining instrument identity despite rapid movements, overlaps, or occlusions, ensuring real-time precision and preventing misplacement.

Objective

This research aims to improve real-time detection and tracking of surgical instruments in minimally invasive surgery (MIS) by integrating a sophisticated deep learning (DL) framework. Utilizing cutting-edge computer vision (CV) techniques, the proposed approach enhances surgical workflows, minimizes errors, and improves patient safety with increased precision and efficiency.

Methods

The framework incorporates YOLOv9n, the latest iteration released in 2024, along with advanced tracking algorithms such as ByteTrack and BoT-SORT to enable real-time detection, tracking, and enumeration of surgical instruments. The YOLOv9n model underwent evaluation against YOLOv8n, YOLOv5n, YOLOv11n, and Faster R-CNN to ensure an optimal balance of speed and accuracy. The study employed the m2cai16-tool-locations detection dataset, utilizing advanced preprocessing and data augmentation techniques to enhance model performance and mitigate dataset imbalances.

Results

Comparative assessments demonstrated that YOLOv9n, when combined with ByteTrack and BoT-SORT, achieved outstanding performance with a Mean Average Precision (mAP50) of 98.4% and an inference speed of 0.3 milliseconds. This system ensures robust tracking even under rapid movements and partial occlusions, significantly improving precision and operational efficiency in MIS.

Conclusion

This automated deep learning solution reduces the cognitive burden on surgical teams, enhances patient safety, and optimizes procedural efficiency. Its potential integration with IoT, mobile applications, and intelligent operating rooms underscores its transformative role in modern surgery, setting new standards for AI-driven laparoscopic procedures and surgical innovation.

Graphic abstract