Synthetic Data Generated by Artificial Intelligence to Optimize Surgical Trial Design

Abstract

OBJECTIVE: This study aimed to assess artificial intelligence (AI)-based synthetic data (SD) generation technology in surgery, evaluating the accuracy of the generated data and comparing the derived outcomes with real-world data. SUMMARY BACKGROUND DATA: Trials evaluating new surgical techniques face numerous challenges. SD can play a pivotal role in optimizing clinical trial design, but must be used alongside real-world data to ensure accuracy. Transanal transection and single-stapled anastomosis (TTSS) is a technique with the potential to decrease the anastomotic leak (AL) rate over the double-stapled (DS) technique, according to preliminary data. METHODS: The original data set included consecutive patients undergoing minimally invasive total mesorectal excision for rectal cancer with DS or TTSS anastomosis between 2010 and 2024. An AI-based generative model was trained to create high-fidelity SD, implemented and tested in a clinical trial setting using the 90-day AL rate as a primary endpoint. RESULTS: We created a synthetic copy of the original cohort (n=653) using the real data to train the model and evaluate its performance using the Synthetic vAlidation FramEwork powered by Train. The comparison between synthetic versus real data demonstrated high statistical fidelity, clinical utility, and privacy preservation. We conditionally generated a balanced cohort (n=1200) with an equal number of patients for both types of anastomoses and strong performances using Synthetic Validation Framework powered by TrainTheSD analysis confirmed real data findings, showing a significantly lower AL rate in the TTSS cohort ( P <0.0001). CONCLUSIONS: AI-generated SD showed a high fidelity in replicating the statistical properties and complexity of the clinical features observed in the real-world population, being a very promising tool to improve surgical research.

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