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Code-free machine learning for object detection in surgical video: a benchmarking, feasibility, and cost study.

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Code-free machine learning (CFML) platforms can identify surgical instruments in videos, outperforming traditional models. This enables surgeons to perform data analysis without coding, advancing surgical data science.

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Area of Science:

  • Neurosurgery
  • Surgical Data Science
  • Machine Learning

Background:

  • Machine learning (ML) analysis typically requires significant technical expertise.
  • Novel platforms offer code-free ML (CFML) deployment, potentially democratizing ML for users without coding experience.
  • The application of CFML to neurosurgical video and surgical data science is not yet established.

Purpose of the Study:

  • To evaluate the performance of a code-free ML (CFML) system for surgical instrument identification in intraoperative endoscopic videos.
  • To compare the instrument-detection capabilities of CFML against state-of-the-art, code-based ML models.
  • To assess the feasibility and advantages of using CFML in surgical video analysis.

Main Methods:

  • A code-free ML (CFML) system was employed to identify surgical instruments in endoscopic, endonasal intraoperative videos from a cadaver model.
  • The CFML model was trained and validated on 31,443 images.
  • Performance was compared to two Python-based ML models (RetinaNet and YOLOv3) using the same dataset.

Main Results:

  • The CFML system successfully processed surgical video data without coding.
  • CFML achieved a mean average precision of 0.708, outperforming RetinaNet (0.669) and YOLOv3 (0.527).
  • Advantages included ease of use, lower cost, and a user-friendly interface; drawbacks included limited model interpretability and customization.

Conclusions:

  • CFML demonstrated baseline performance exceeding standard code-based object detection networks in surgical video analysis.
  • This approach is promising for surgeon-scientists for clinical questions, quality improvement, and research.
  • While interpretability and customization challenges remain, CFML platforms will grow in importance for rapid, efficient ML analysis by surgeons.