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Prediction of laparoscopic procedure duration using unlabeled, multimodal sensor data.

Sebastian Bodenstedt1, Martin Wagner2, Lars Mündermann3

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Predicting surgical procedure duration is crucial for scheduling. This study developed a method using endoscopic video and device data to estimate remaining surgery time, improving accuracy with combined visual and sensor information.

Keywords:
Duration predictionSensorORSurgical workflow analyses

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Area of Science:

  • Medical technology
  • Computer vision
  • Surgical informatics

Background:

  • Surgical procedure duration is unpredictable, complicating operating room scheduling.
  • Accurate real-time duration estimation requires analyzing the intervention's current state.

Purpose of the Study:

  • To develop a context-aware method for online prediction of surgical procedure duration.
  • To alleviate scheduling difficulties caused by unpredictable surgery lengths.

Main Methods:

  • Utilized convolutional neural networks (CNNs) for continuous duration prediction.
  • Analyzed unlabeled data from endoscopic video streams and surgical device data.
  • Compared methods based on vision, device data, and a combination of both.

Main Results:

  • The combined method, using both video and device data, achieved the best performance.
  • Overall average error was 37%, with an average halftime error of approximately 28%.

Conclusions:

  • Presented the first approach for online procedure duration prediction in laparoscopy using unlabeled video and device data.
  • Incorporating visual data significantly improves prediction accuracy compared to device data alone.
  • The visual channel is critical for accurate surgical duration estimation.