Machine Learning‐Enhanced Optimization for High‐Throughput Precision in Cellular Droplet Bioprinting
Abstract Organoids produce through traditional manual pipetting methods face challenges such as labor‐intensive procedures and batch‐to‐batch variability in quality. To ensure consistent organoid production, 3D bioprinting platforms offer a more efficient alternative. However, optimizing multiple pr...
Saved in:
| Main Authors: | , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2025-05-01
|
| Series: | Advanced Science |
| Subjects: | |
| Online Access: | https://doi.org/10.1002/advs.202412831 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849694165641723904 |
|---|---|
| author | Jaemyung Shin Ryan Kang Kinam Hyun Zhangkang Li Hitendra Kumar Kangsoo Kim Simon S. Park Keekyoung Kim |
| author_facet | Jaemyung Shin Ryan Kang Kinam Hyun Zhangkang Li Hitendra Kumar Kangsoo Kim Simon S. Park Keekyoung Kim |
| author_sort | Jaemyung Shin |
| collection | DOAJ |
| description | Abstract Organoids produce through traditional manual pipetting methods face challenges such as labor‐intensive procedures and batch‐to‐batch variability in quality. To ensure consistent organoid production, 3D bioprinting platforms offer a more efficient alternative. However, optimizing multiple printing parameters to achieve the desired organoid size remains a time‐consuming and costly endeavor. To address these obstacles, machine learning is employed to optimize five critical printing parameters (i.e., bioink viscosity, nozzle size, printing time, printing pressure, and cell concentration), and develop algorithms capable of immediate cellular droplet size prediction. In this study, a high‐throughput cellular droplet bioprinter is designed, capable of printing over 50 cellular droplets simultaneously, producing the large dataset required for effective machine learning training. Among the five algorithms evaluated, the multilayer perceptron model demonstrates the highest prediction accuracy, while the decision tree model offers the fastest computation time. Finally, these top‐performing machine learning models are integrated into a user‐friendly interface to streamline usability. The bioprinting parameter optimization platform develops in this study is expected to create significant synergy when combined with various bioprinting technologies, advancing the scalable production of organoids for a range of applications. |
| format | Article |
| id | doaj-art-cc7fd96d0dc54eaeb583e231dbc19f65 |
| institution | DOAJ |
| issn | 2198-3844 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Wiley |
| record_format | Article |
| series | Advanced Science |
| spelling | doaj-art-cc7fd96d0dc54eaeb583e231dbc19f652025-08-20T03:20:10ZengWileyAdvanced Science2198-38442025-05-011220n/an/a10.1002/advs.202412831Machine Learning‐Enhanced Optimization for High‐Throughput Precision in Cellular Droplet BioprintingJaemyung Shin0Ryan Kang1Kinam Hyun2Zhangkang Li3Hitendra Kumar4Kangsoo Kim5Simon S. Park6Keekyoung Kim7Department of Biomedical Engineering Schulich School of Engineering University of Calgary Calgary Alberta T2N 1N4 CanadaDepartment of Electrical and Software Engineering Schulich School of Engineering University of Calgary Calgary Alberta T2N 1N4 CanadaDepartment of Mechanical and Manufacturing Engineering Schulich School of Engineering University of Calgary Calgary Alberta T2N 1N4 CanadaDepartment of Biomedical Engineering Schulich School of Engineering University of Calgary Calgary Alberta T2N 1N4 CanadaDepartment of Biosciences and Biomedical Engineering Indian Institute of Technology Indore Indore Madhya Pradesh 453552 IndiaDepartment of Electrical and Software Engineering Schulich School of Engineering University of Calgary Calgary Alberta T2N 1N4 CanadaDepartment of Mechanical and Manufacturing Engineering Schulich School of Engineering University of Calgary Calgary Alberta T2N 1N4 CanadaDepartment of Biomedical Engineering Schulich School of Engineering University of Calgary Calgary Alberta T2N 1N4 CanadaAbstract Organoids produce through traditional manual pipetting methods face challenges such as labor‐intensive procedures and batch‐to‐batch variability in quality. To ensure consistent organoid production, 3D bioprinting platforms offer a more efficient alternative. However, optimizing multiple printing parameters to achieve the desired organoid size remains a time‐consuming and costly endeavor. To address these obstacles, machine learning is employed to optimize five critical printing parameters (i.e., bioink viscosity, nozzle size, printing time, printing pressure, and cell concentration), and develop algorithms capable of immediate cellular droplet size prediction. In this study, a high‐throughput cellular droplet bioprinter is designed, capable of printing over 50 cellular droplets simultaneously, producing the large dataset required for effective machine learning training. Among the five algorithms evaluated, the multilayer perceptron model demonstrates the highest prediction accuracy, while the decision tree model offers the fastest computation time. Finally, these top‐performing machine learning models are integrated into a user‐friendly interface to streamline usability. The bioprinting parameter optimization platform develops in this study is expected to create significant synergy when combined with various bioprinting technologies, advancing the scalable production of organoids for a range of applications.https://doi.org/10.1002/advs.202412831bioprintingcellular dropletsmachine learningoptimization |
| spellingShingle | Jaemyung Shin Ryan Kang Kinam Hyun Zhangkang Li Hitendra Kumar Kangsoo Kim Simon S. Park Keekyoung Kim Machine Learning‐Enhanced Optimization for High‐Throughput Precision in Cellular Droplet Bioprinting Advanced Science bioprinting cellular droplets machine learning optimization |
| title | Machine Learning‐Enhanced Optimization for High‐Throughput Precision in Cellular Droplet Bioprinting |
| title_full | Machine Learning‐Enhanced Optimization for High‐Throughput Precision in Cellular Droplet Bioprinting |
| title_fullStr | Machine Learning‐Enhanced Optimization for High‐Throughput Precision in Cellular Droplet Bioprinting |
| title_full_unstemmed | Machine Learning‐Enhanced Optimization for High‐Throughput Precision in Cellular Droplet Bioprinting |
| title_short | Machine Learning‐Enhanced Optimization for High‐Throughput Precision in Cellular Droplet Bioprinting |
| title_sort | machine learning enhanced optimization for high throughput precision in cellular droplet bioprinting |
| topic | bioprinting cellular droplets machine learning optimization |
| url | https://doi.org/10.1002/advs.202412831 |
| work_keys_str_mv | AT jaemyungshin machinelearningenhancedoptimizationforhighthroughputprecisionincellulardropletbioprinting AT ryankang machinelearningenhancedoptimizationforhighthroughputprecisionincellulardropletbioprinting AT kinamhyun machinelearningenhancedoptimizationforhighthroughputprecisionincellulardropletbioprinting AT zhangkangli machinelearningenhancedoptimizationforhighthroughputprecisionincellulardropletbioprinting AT hitendrakumar machinelearningenhancedoptimizationforhighthroughputprecisionincellulardropletbioprinting AT kangsookim machinelearningenhancedoptimizationforhighthroughputprecisionincellulardropletbioprinting AT simonspark machinelearningenhancedoptimizationforhighthroughputprecisionincellulardropletbioprinting AT keekyoungkim machinelearningenhancedoptimizationforhighthroughputprecisionincellulardropletbioprinting |