⚡ Quick Summary
This study developed an artificial intelligence (AI) model to track otologic instruments in mastoidectomy videos, achieving impressive results with a precision of 0.93 for drill tracking. The model’s near-real-time processing speed opens new avenues for analyzing surgical skills objectively.
🔍 Key Details
- 📊 Dataset: 13 videos of cadaveric mastoidectomies
- 👨⚕️ Participants: 6 otolaryngology residents and 1 senior neurotology attending
- ⚙️ Technology: YOLOv8 AI computer vision model
- 🏆 Performance: Drill tracking: Precision 0.93, Recall 0.89, mAP50 0.93; Suction irrigator: Precision 0.67, Recall 0.61, mAP50 0.62
- ⏱️ Prediction speed: ~100 milliseconds per image
🔑 Key Takeaways
- 🤖 AI model effectively tracks surgical instruments in real-time.
- 🏆 High accuracy achieved for drill tracking, with precision at 0.93.
- 📉 Lower performance noted for suction irrigator tracking.
- 📈 Increased drill speed observed in attending surgeons compared to residents.
- 🔍 Automated tracking eliminates the need for manual annotation.
- 🌐 Potential applications in navigation and augmented reality surgical environments.
- 📅 Study conducted at a tertiary care center.
📚 Background
The integration of artificial intelligence in surgical settings is rapidly evolving, with the potential to enhance surgical training and performance assessment. Traditional methods of tracking surgical instruments often rely on manual annotation, which can be time-consuming and subjective. This study aims to leverage AI technology to provide a more efficient and objective means of tracking instruments during complex procedures like mastoidectomy.
🗒️ Study
Conducted at a tertiary care center, this retrospective case series involved recording thirteen 30-minute videos of cadaveric mastoidectomies performed by otolaryngology residents. The study utilized the YOLOv8 AI model to track the suction irrigator and drill, with the videos divided into training, validation, and test sets to evaluate the model’s performance.
📈 Results
The AI model demonstrated excellent performance in tracking the drill, achieving a precision of 0.93, recall of 0.89, and mean average precision (mAP50) of 0.93. In contrast, the suction irrigator showed lower performance metrics, with precision at 0.67 and recall at 0.61. The model’s prediction speed was notably fast, averaging around 100 milliseconds per image.
🌍 Impact and Implications
The findings from this study have significant implications for the future of surgical training and assessment. By enabling automated tracking of surgical instruments, this AI model can facilitate the analysis of objective metrics related to surgical skill. This advancement could lead to improved training methodologies and enhanced surgical outcomes, particularly in complex procedures like mastoidectomy.
🔮 Conclusion
This study highlights the transformative potential of artificial intelligence in the field of otolaryngology. The ability to track surgical instruments with high accuracy and speed not only streamlines the evaluation process but also paves the way for future innovations in surgical navigation and augmented reality environments. Continued research in this area is essential to fully realize the benefits of AI in surgical practice.
💬 Your comments
What are your thoughts on the use of AI in surgical settings? Do you see potential for further applications in other medical fields? 💬 Share your insights in the comments below or connect with us on social media:
Artificial Intelligence Tracking of Otologic Instruments in Mastoidectomy Videos.
Abstract
OBJECTIVE: Develop an artificial intelligence (AI) model to track otologic instruments in mastoidectomy videos.
STUDY DESIGN: Retrospective case series.
SETTING: Tertiary care center.
SUBJECTS: Six otolaryngology residents (PGY 3-5) and one senior neurotology attending.
INTERVENTIONS: Thirteen 30-minute videos of cadaveric mastoidectomies were recorded by residents. The suction irrigator and drill were semi-manually annotated. Videos were split into training (N = 8), validation (N = 3), and test (N = 2) sets. YOLOv8, a state-of-the-art AI computer vision model, was adapted to track the instruments.
MAIN OUTCOME MEASURES: Precision, recall, and mean average precision using an intersection over union cutoff of 50% (mAP50). Drill speed in two prospectively collected live mastoidectomy videos by a resident and attending surgeon.
RESULTS: The model achieved excellent performance for tracking the drill (precision 0.93, recall 0.89, and mAP50 0.93) and low performance for the suction irrigator (precision 0.67, recall 0.61, and mAP50 0.62) in test videos. Prediction speed was fast (~100 milliseconds per image). Predictions on prospective videos revealed higher mean drill speed (8.6 ± 5.7 versus 7.6 ± 7.4 mm/s, respectively; mean ± SD; p < 0.01) and duration of high drill speed (>15 mm/s; p < 0.05) in attending than resident surgery.
CONCLUSIONS: An AI model can track the drill in mastoidectomy videos with high accuracy and near-real-time processing speed. Automated tracking opens the door to analyzing objective metrics of surgical skill without the need for manual annotation and will provide valuable data for future navigation and augmented reality surgical environments.
Author: [‘Liu GS’, ‘Parulekar S’, ‘Lee MC’, ‘El Chemaly T’, ‘Diop M’, ‘Park R’, ‘Blevins NH’]
Journal: Otol Neurotol
Citation: Liu GS, et al. Artificial Intelligence Tracking of Otologic Instruments in Mastoidectomy Videos. Artificial Intelligence Tracking of Otologic Instruments in Mastoidectomy Videos. 2024; (unknown volume):(unknown pages). doi: 10.1097/MAO.0000000000004330