Predictive Modeling of Oxygen Gradient in Gut-on-a-Chip Using Machine Learning and Finite Element Simulation

The FDA plans to gradually replace animal testing with organoid and organ-on-a-chip technologies for drug safety assessment, driving surging demand for gut-on-a-chip in food and drug safety evaluation and highlighting the need for efficient, precise chip designs. Oxygen gradients are central to these devices because they shape epithelial metabolism, microbial co-culture, and overall gut homeostasis. We coupled machine learning with finite element analysis to build a parametric COMSOL Multiphysics model linking channel geometry, transport coefficients, and cellular oxygen uptake to the resulting oxygen field. For numerical prediction, three models—Random Forest (RF), XGBoost, and MLP—were employed, with XGBoost achieving the highest accuracy (RMSE = 1.68%). SHAP analysis revealed that medium flow rate (39.7%), external flux (26.9%), and cellular oxygen consumption rate (24.8%) contributed most importantly to the prediction. For oxygen distribution mapping, an innovative Boundary-Guided Generative Network (BG-Net) model was employed, yielding an average concentration error of 0.012 mol/m3 (~4.8%), PSNR of 33.71 dB, and SSIM of 0.9220, demonstrating excellent image quality. Ablation experiment verified the necessity of each architectural component of BG-Net. This pipeline offers quantitative, data-driven guidance for tuning oxygen gradients in gut-on-a-chip. Future work will explore extensions including real experimental data integration, real-time prediction, and multi-task scenarios.