Deep Learning for Predicting Spheroid Viability: Novel Convolutional Neural Network Model for Automating Quality Control for Three-Dimensional Bioprinting

• Published in: Bioengineering (Basel) Vol. 12; no. 1; p. 28
• Main Authors: Sheikh, Zyva A., Clarke, Oliver, Mir, Amatullah, Hibino, Narutoshi
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 01.01.2025; MDPI
• Subjects: 3-D printers; 3D printing; 3D-bioprinting; Artificial neural networks; Automation; Bioengineering; Biomedical materials; Bioprinting; Cancer; Cardiovascular diseases; Care and treatment; Cell size; Cholecystokinin; convolutional neural networks; Coronary artery disease; Deep learning; Evaluation; Heart diseases; Human bias; Hypoxia; Machine learning; Medical research; Mesenchymal stem cells; Methods; Neural networks; Physiology; prediction; Predictive control; Production processes; Quality control; spheroid; Spheroids; Stem cells; Three dimensional printing; Tissue engineering; Transplants & implants; Viability; United States; deep learning; tissue biofabrication; prediction; 3D-bioprinting; convolutional neural networks; spheroid; viability
• ISSN: 2306-5354; 2306-5354
• DOI: 10.3390/bioengineering12010028

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.