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A New Index for the Quantitative Evaluation of Surgical Invasiveness Based on Perioperative Patients' Behavior

Kozo Nakanishi1, Hidenori Goto1

  • 1Department of General Thoracic Surgery, National Hospital Organization Saitama Hospital, Wako Saitama, Japan.

JMIR Perioperative Medicine
|November 14, 2023
PubMed
Summary

Machine learning accurately predicted patient actions using acceleration data, creating a novel index of behavior pattern (iBP) to quantify surgical invasiveness and perioperative recovery. This method offers a new way to assess recovery after minimally invasive surgery.

Keywords:
AIVATSartificial intelligencehuman activity recognitioninvasivenessmachine learningmobile phonepatient-oriented outcomeperioperative managementpostoperative recoverysurgerytriaxial accelerationvideo-assisted thoracoscopic surgery

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Area of Science:

  • Medical technology
  • Machine learning in healthcare
  • Surgical innovation

Background:

  • Assessing surgical invasiveness and patient recovery post-thoracoscopic surgery is challenging due to the lack of reliable evaluation methods.
  • Patient recovery is hypothesized to correlate with surgical invasiveness, defined by behavioral changes during the perioperative period.

Purpose of the Study:

  • To evaluate the efficacy of machine learning algorithms utilizing triaxial acceleration data for capturing perioperative behavioral changes.
  • To establish a quantitative index for assessing variations in surgical invasiveness based on patient behavior.

Main Methods:

  • Trained and selected top-performing machine learning models (e.g., light-gradient boosting) using a public human acceleration dataset.
  • Collected chest sensor acceleration data from two patients undergoing different levels of thoracoscopic surgery.
  • Developed a 2D index of behavior pattern (iBP) based on daily action probabilities (walking, standing, sitting, lying down) and analyzed iBP variations to correlate with surgical invasiveness.

Main Results:

  • A light-gradient boosting model achieved high accuracy (0.98) in predicting patient actions.
  • Significant differences in the index of behavior pattern (iBP) were observed between patients, with greater enclosed areas and distances for the more invasive procedure (lung lobectomy).
  • The study found statistically significant differences in behavioral indices between patients (P=.03), correlating with surgical invasiveness.

Conclusions:

  • Machine learning accurately predicts patient actions from acceleration data, offering insights into perioperative behavior.
  • The developed index of behavior pattern (iBP) effectively visualizes and quantifies perioperative changes and differences in surgical invasiveness.
  • This approach holds potential for improved assessment of patient recovery and surgical procedure comparison.