Deep Learning for Predicting Spheroid Viability: Novel Convolutional Neural Network Model for Automating Quality Control for Three-Dimensional Bioprinting
Spheroids serve as the building blocks for three-dimensional (3D) bioprinted tissue patches. When larger than 500 μm, the desired size for 3D bioprinting, they tend to have a hypoxic core with necrotic cells. Therefore, it is critical to assess the viability of spheroids in order to ensure the succe...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-01-01
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| Series: | Bioengineering |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2306-5354/12/1/28 |
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| Summary: | Spheroids serve as the building blocks for three-dimensional (3D) bioprinted tissue patches. When larger than 500 μm, the desired size for 3D bioprinting, they tend to have a hypoxic core with necrotic cells. Therefore, it is critical to assess the viability of spheroids in order to ensure the successful fabrication of high-viability patches. However, current viability assays are time-consuming, labor-intensive, require specialized training, or are subject to human bias. In this study, we build a convolutional neural network (CNN) model to efficiently and accurately predict spheroid viability, using a phase-contrast image of a spheroid as its input. A comprehensive dataset of mouse mesenchymal stem cell (mMSC) spheroids of varying sizes with corresponding viability percentages, which was obtained through CCK-8 assays, was established and used to train and validate the model. The model was trained to automatically classify spheroids into one of four distinct categories based on their predicted viability: 0–20%, 20–40%, 40–70%, and 70–100%. The model achieved an average accuracy of 92%, with a consistent loss below 0.2. This deep-learning model offers a non-invasive, efficient, and accurate method to streamline the assessment of spheroid quality, thereby accelerating the development of bioengineered cardiac tissue patches for cardiovascular disease therapies. |
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| ISSN: | 2306-5354 |