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Artificial intelligence (AI) generated synthetic data (SD) accurately mirrors real-world surgical data, confirming transanal transection and single-stapled anastomosis (TTSS) reduces anastomotic leak (AL) rates. This AI tool enhances surgical research by improving clinical trial design and data fidelity.

Keywords:
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Area of Science:

  • Surgical innovation and clinical trial methodology.
  • Application of artificial intelligence in medical research.

Background:

  • Clinical trials for new surgical techniques face significant hurdles.
  • Synthetic data (SD) offers potential for optimizing trial design when validated with real-world data.
  • Transanal transection and single-stapled anastomosis (TTSS) shows promise in reducing anastomotic leak (AL) rates compared to double-stapled (DS) techniques.

Purpose of the Study:

  • To evaluate the accuracy and utility of AI-based synthetic data generation in surgical research.
  • To compare outcomes derived from synthetic data with real-world patient data.
  • To assess the potential of AI-generated SD in clinical trial settings.

Main Methods:

  • Trained an AI generative model on a real-world dataset of minimally invasive Total Mesorectal Excision patients (2010-2024).
  • Utilized the Synthetic vAlidation FramEwork powered by Train (SAFE) to assess data fidelity, clinical utility, and privacy.
  • Generated both a synthetic copy of the original cohort and a balanced cohort for analysis.

Main Results:

  • AI-generated SD demonstrated high statistical fidelity, clinical utility, and privacy preservation compared to real data.
  • Analysis of the synthetic data confirmed real-world findings: TTSS significantly lowered the AL rate (P<0.0001).
  • A balanced synthetic cohort (n=1200) showed strong performance metrics using SAFE.

Conclusions:

  • AI-generated synthetic data accurately replicates complex clinical features and statistical properties of real-world patient populations.
  • This technology shows significant promise as a tool to enhance and accelerate surgical research.
  • Synthetic data can effectively support the evaluation of surgical techniques and clinical trial design.