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Surgical phase modelling in minimal invasive surgery.

F C Meeuwsen1, F van Luyn2, M D Blikkendaal3

  • 1Department of Biomechanical Engineering, Delft University of Technology, Mekelweg 2, 2628 CD, Delft, The Netherlands. F.C.Meeuwsen@tudelft.nl.

Surgical Endoscopy
|September 7, 2018
PubMed
Summary

Surgical process modelling accurately recognized surgical phases in 40 laparoscopic hysterectomies using instrument data. This approach also enabled reliable prediction of surgical end-times, improving OR logistics.

Keywords:
HysterectomyInstrument trackingPatient safetyPhase recognitionWorkflow

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Area of Science:

  • Medical Informatics
  • Surgical Engineering
  • Artificial Intelligence in Medicine

Background:

  • Surgical Process Modelling (SPM) enhances understanding of surgical workflows for improved operating room (OR) logistics and patient care.
  • Most SPM studies focus on laparoscopic cholecystectomy; broader applicability requires analysis of more complex procedures like laparoscopic hysterectomies (LH).
  • LH procedures exhibit significant variability in duration, presenting a challenge for accurate workflow analysis.

Purpose of the Study:

  • To investigate the accuracy of recognizing and extracting surgical phases in laparoscopic hysterectomies (LH).
  • To demonstrate the applicability of SPM in complex surgical procedures with inherent time variability.
  • To utilize the developed model for automatic prediction of surgical end-times in LH.

Main Methods:

  • A dataset of 40 video-recorded LH procedures was manually annotated for instrument use.
  • Instrument usage data was used as input features for a Random Forest model trained for surgical phase recognition.
  • Tenfold cross-validation was employed to optimize the model for predicting surgical end-times.

Main Results:

  • The Random Forest model achieved 77% accuracy in recognizing surgical phases, with six phases recognized accurately over 80% of their duration.
  • Significant variability was observed within specific surgical phases of LH.
  • Surgical end-time prediction resulted in an average error of 16 ± 13 minutes throughout the procedures.

Conclusions:

  • An intra-operative approach for surgical phase recognition in LH was successfully demonstrated using instrument usage data.
  • The developed model effectively automates surgical phase detection.
  • The study confirms the model's capability to generate reliable predictions of surgical end-times.