ArticleFrontiers in bioengineering and biotechnology2021
Morphology-Based Deep Learning Approach for Predicting Osteogenic Differentiation.
Article in Frontiers in bioengineering and biotechnology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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8 citing papers in PubMed.
- Development and validation of deep learning for predicting the growth of ovarian cancer organoids.Chinese medical journal · 2026Article
- Non-invasive quantification of viability in liver spheroids using deep learning.Frontiers in bioengineering and biotechnology · 2026Article
- OsteoNet: A deep learning framework linking cellular morphology to molecular markers for quantifying osteogenic differentiation.Computational and structural biotechnology journal · 2026Article
- Current Trends and Future Opportunities of AI-Based Analysis in Mesenchymal Stem Cell Imaging: A Scoping Review.Journal of imaging · 2025Review
- Artificial Intelligence (AI): A Potential Game Changer in Regenerative Orthopedics-A Scoping Review.Indian journal of orthopaedics · 2024Article
- Stem cell-based approaches for developmental neurotoxicity testing.Frontiers in toxicology · 2024Review
- Artificial Intelligence Supports Automated Characterization of Differentiated Human Pluripotent Stem Cells.Stem cells (Dayton, Ohio) · 2023Article
- Morphology-based deep learning approach for predicting adipogenic and osteogenic differentiation of human mesenchymal stem cells (hMSCs).Frontiers in cell and developmental biology · 2023Article
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7 authors.
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Abstract
Early, high-throughput, and accurate recognition of osteogenic differentiation of stem cells is urgently required in stem cell therapy, tissue engineering, and regenerative medicine. In this study, we established an automatic deep learning algorithm, i.e., osteogenic convolutional neural network (OCNN), to quantitatively measure the osteogenic differentiation of rat bone marrow mesenchymal stem cells (rBMSCs). rBMSCs stained with F-actin and DAPI during early differentiation (day 0, 1, 4, and 7) were captured using laser confocal scanning microscopy to train OCNN. As a result, OCNN successfully distinguished differentiated cells at a very early stage (24 h) with a high area under the curve (AUC) (0.94 ± 0.04) and correlated with conventional biochemical markers. Meanwhile, OCNN exhibited better prediction performance compared with the single morphological parameters and support vector machine. Furthermore, OCNN successfully predicted the dose-dependent effects of small-molecule osteogenic drugs and a cytokine. OCNN-based online learning models can further recognize the osteogenic differentiation of rBMSCs cultured on several material surfaces. Hence, this study initially demonstrated the foreground of OCNN in osteogenic drug and biomaterial screening for next-generation tissue engineering and stem cell research.
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