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Related Experiment Video

Updated: Jul 20, 2026

Author Spotlight: Revolutionizing Remote Surgery with Augmented Reality and Robotics for Enhanced Precision and Accessibility

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

Priyanka Anup Ujjainkar¹, Shital A Raut²

¹Computer Science and Engineering, Visvesvaraya National Institute of Technology, Nagpur, Maharashtra, 440010, India. dt23cse007@students.vnit.ac.in.

Surgical Endoscopy

|June 30, 2025

Summary

This study introduces an advanced deep learning system for real-time surgical instrument tracking in minimally invasive surgery (MIS). The AI framework significantly enhances precision and efficiency, improving patient safety during procedures.

Keywords:

BoT-SORT ByteTrack Deep learning Instrument detection Laparoscopic surgery Tracking YOLO

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Area of Science:

Medical Technology
Computer Vision
Artificial Intelligence

Background:

Accurate tracking of surgical instruments is crucial for patient safety and efficiency in laparoscopic procedures.
Advanced tracking systems improve object detection, maintaining instrument identity during complex surgical movements.

Purpose of the Study:

To enhance real-time detection and tracking of surgical instruments in minimally invasive surgery (MIS) using a deep learning (DL) framework.
To improve surgical workflows, minimize errors, and increase patient safety through enhanced precision and efficiency.

Main Methods:

Integrated YOLOv9n (2024) with ByteTrack and BoT-SORT for real-time instrument detection and tracking.
Evaluated YOLOv9n against other models (YOLOv8n, YOLOv5n, YOLOv11n, Faster R-CNN) for optimal speed and accuracy.
Utilized the m2cai16-tool-locations dataset with advanced preprocessing and data augmentation.

Main Results:

The YOLOv9n system with ByteTrack and BoT-SORT achieved 98.4% mAP50 and 0.3 ms inference speed.
Demonstrated robust tracking performance under rapid movements and partial occlusions.
Significantly improved precision and operational efficiency in MIS procedures.

Conclusions:

The automated DL solution reduces cognitive load on surgical teams and enhances patient safety.
Potential integration with IoT and intelligent operating rooms signifies a transformative role in AI-driven surgery.
Sets new standards for AI in laparoscopic procedures and surgical innovation.