Data-driven approaches for complex systems: leveraging machine learning, materials science, and manufacturing for new biomedical technologies
Many research efforts to advance human health and well-being involve interdisciplinary problem spaces and complex, poorly-understood systems. This thesis integrates both computational and experimental approaches to advance our understanding and control of complex systems at the interface of machine...
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| Format: | Thesis |
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Massachusetts Institute of Technology
2023
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| Online Access: | https://hdl.handle.net/1721.1/151419 |
| _version_ | 1811074620310159360 |
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| author | Verheyen, Connor Anthony |
| author2 | Roche, Ellen T. |
| author_facet | Roche, Ellen T. Verheyen, Connor Anthony |
| author_sort | Verheyen, Connor Anthony |
| collection | MIT |
| description | Many research efforts to advance human health and well-being involve interdisciplinary problem spaces and complex, poorly-understood systems. This thesis integrates both computational and experimental approaches to advance our understanding and control of complex systems at the interface of machine learning, materials science, and manufacturing. Specifically, I demonstrate the data-driven description of supervised machine learning for biomedical engineering tasks, the data-driven design of optimized soft granular biomaterials, and the proof-of-concept development of a transcatheter additive manufacturing platform.
In Part 1, I develop custom software for high-resolution, multifactorial machine learning (ML) experiments. I iteratively apply this workflow to a set of diverse ML problems from the biomedical engineering (BME) domain to generate massive meta-datasets covering each phase of the hierarchical ML optimization and evaluation process. Then, I describe the underlying patterns and heterogeneity in these rich datasets and delineate empirical guidelines for the rigorous and reliable adoption of machine learning for BME problems.
In Part 2, I leverage the insights from Part 1 to develop a flexible and robust data-driven modeling pipeline for complex soft materials. The pipeline can be applied after each round of experimentation to build predictive models, extract key design rules, and generate data-driven design frameworks. I use this integrated, stepwise approach to optimize the structures, properties, and performance profiles of soft granular biomaterials for injection- and extrusion-based biomedical applications.
In Part 3, I leverage the optimized materials from Part 2 to develop a novel microgel-based transcatheter additive manufacturing technology. I obtain proof-of-concept data for the platform's critical features, including controlled transcatheter material delivery to distant target locations, rapid in situ structuration of arbitrary 3D constructs, and reliable scaffold stabilization to ensure long-term implant integrity. Together, this work paves the way for minimally-invasive, patient-specific, in situ biofabrication. |
| first_indexed | 2024-09-23T09:52:42Z |
| format | Thesis |
| id | mit-1721.1/151419 |
| institution | Massachusetts Institute of Technology |
| last_indexed | 2024-09-23T09:52:42Z |
| publishDate | 2023 |
| publisher | Massachusetts Institute of Technology |
| record_format | dspace |
| spelling | mit-1721.1/1514192023-08-01T04:08:34Z Data-driven approaches for complex systems: leveraging machine learning, materials science, and manufacturing for new biomedical technologies Verheyen, Connor Anthony Roche, Ellen T. Harvard-MIT Program in Health Sciences and Technology Many research efforts to advance human health and well-being involve interdisciplinary problem spaces and complex, poorly-understood systems. This thesis integrates both computational and experimental approaches to advance our understanding and control of complex systems at the interface of machine learning, materials science, and manufacturing. Specifically, I demonstrate the data-driven description of supervised machine learning for biomedical engineering tasks, the data-driven design of optimized soft granular biomaterials, and the proof-of-concept development of a transcatheter additive manufacturing platform. In Part 1, I develop custom software for high-resolution, multifactorial machine learning (ML) experiments. I iteratively apply this workflow to a set of diverse ML problems from the biomedical engineering (BME) domain to generate massive meta-datasets covering each phase of the hierarchical ML optimization and evaluation process. Then, I describe the underlying patterns and heterogeneity in these rich datasets and delineate empirical guidelines for the rigorous and reliable adoption of machine learning for BME problems. In Part 2, I leverage the insights from Part 1 to develop a flexible and robust data-driven modeling pipeline for complex soft materials. The pipeline can be applied after each round of experimentation to build predictive models, extract key design rules, and generate data-driven design frameworks. I use this integrated, stepwise approach to optimize the structures, properties, and performance profiles of soft granular biomaterials for injection- and extrusion-based biomedical applications. In Part 3, I leverage the optimized materials from Part 2 to develop a novel microgel-based transcatheter additive manufacturing technology. I obtain proof-of-concept data for the platform's critical features, including controlled transcatheter material delivery to distant target locations, rapid in situ structuration of arbitrary 3D constructs, and reliable scaffold stabilization to ensure long-term implant integrity. Together, this work paves the way for minimally-invasive, patient-specific, in situ biofabrication. Ph.D. 2023-07-31T19:38:18Z 2023-07-31T19:38:18Z 2023-06 2023-06-13T20:51:59.276Z Thesis https://hdl.handle.net/1721.1/151419 0000-0003-3408-7055 In Copyright - Educational Use Permitted Copyright retained by author(s) https://rightsstatements.org/page/InC-EDU/1.0/ application/pdf Massachusetts Institute of Technology |
| spellingShingle | Verheyen, Connor Anthony Data-driven approaches for complex systems: leveraging machine learning, materials science, and manufacturing for new biomedical technologies |
| title | Data-driven approaches for complex systems:
leveraging machine learning, materials science, and
manufacturing for new biomedical technologies |
| title_full | Data-driven approaches for complex systems:
leveraging machine learning, materials science, and
manufacturing for new biomedical technologies |
| title_fullStr | Data-driven approaches for complex systems:
leveraging machine learning, materials science, and
manufacturing for new biomedical technologies |
| title_full_unstemmed | Data-driven approaches for complex systems:
leveraging machine learning, materials science, and
manufacturing for new biomedical technologies |
| title_short | Data-driven approaches for complex systems:
leveraging machine learning, materials science, and
manufacturing for new biomedical technologies |
| title_sort | data driven approaches for complex systems leveraging machine learning materials science and manufacturing for new biomedical technologies |
| url | https://hdl.handle.net/1721.1/151419 |
| work_keys_str_mv | AT verheyenconnoranthony datadrivenapproachesforcomplexsystemsleveragingmachinelearningmaterialsscienceandmanufacturingfornewbiomedicaltechnologies |