Synthetic data generation from a stenosed carotid arteries using sensitivity analysis: an application to AI models

In artificial intelligence (AI), data dramatically impacts AI models performance, accuracy, and reliability. High-quality data enables models to make better predictions and produce more reliable outcomes. Poor data quality or lack of data can lead to flawed results and cause poor performance and predictions. Sensitivity analysis could play a vital role to generate synthetic dataset from any validated model. Within this work, a sensitivity-based approach is used to generate synthetic data or measurements for healthy and stenosed carotid arteries. Pressure and flow time-series are collected for different levels of stenosis ranging from $30\%$ to $90\%\text{,}$ which are created artificially from a validated model of the stenosed carotid arteries. Based on the generated dataset, a machine learning model could be developed for new data to detect the site and level of stenosis in the carotid arteries. In order to validate the applicability of the generated dataset for AI-driven analysis, a proof-of-concept Random Forest classifier was trained to classify different levels of stenoses (mild, moderate, and severe) using a $70\%n30\%$ train-test split. The model achieved an overall accuracy of $99.81\%\text{,}$ demonstrating strong discriminative capability of the simulated pressure and flow time-series. Although this high performance is obtained under idealized synthetic conditions without measurement noise, the results confirm that sensitivity-based synthetic data generation can effectively support machine learning applications for carotid stenosis detection and severity assessment.