Deep learning predicts osteogenic differentiation stages of human mesenchymal stem cells from phase-contrast microscopy images

Abstract

In this study, we constructed and validated deep learning models capable of predicting the osteogenic differentiation stages of mesenchymal stem cells (MSCs) using only phase-contrast microscopy images. UE7T-13, an immortalized human MSC line, was cultured in osteoinductive medium. Phase-contrast microscopy images were acquired at D0, D1, D5, D10, and D14 of differentiation. Two deep learning models, ResNet-50 and DenseNet-121, were trained to perform multi-class classification of osteogenic differentiation stages. Model performance was evaluated using precision, sensitivity, F1 score, and overall accuracy. The overall accuracy of the ResNet-50 model was 0.700 and that of the DenseNet-121 model was 0.684. The highest F1 scores occurred at D5, which may reflect more distinctive morphological features during mid-stage differentiation. Our results suggest that deep learning has the potential to non-invasively identify osteogenic differentiation stages based on morphological features alone.