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Peer-reviewedCC BYNano letters

Machine Learning-Enabled Meta-Analysis of Bioengineering Micro- and Nanorobots.

  • Ehsan Rahimi1
  • Mario Palacios-Corella2
  • Mohammad Rahimi3
  • Lucky Odirile Mohutsiwa4
  • Josep Puigmartí-Luis2,5
  • Salvador Pané4
5 affiliations
  1. 1Faculty of Mechanical Engineering (ME), Delft University of Technology, Mekelweg 2, 2628 CDDelft, The Netherlands.
  2. 2Departament de Ciència dels Materials i Química Física, Institut de Química Teòrica i Computacional, Universitat de Barcelona, Barcelona08028, Spain.
  3. 3Department of Mechanical Engineering, McMaster University, Hamilton, ONL8S 4L7, Canada.
  4. 4Multi-Scale Robotics Lab, Institute of Robotics and Intelligent Systems, Department of Mechanical and Process Engineering, ETH Zurich, Tannenstrasse 3, Zurich8092, Switzerland.
  5. 5Institució Catalana de Recerca i Estudis Avançats (ICREA), Pg. Lluís Companys 23, Barcelona08010, Spain.
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Abstract

Micro- and nanorobots are emerging bioengineering tools for targeted delivery, biosensing, imaging, and minimally invasive therapy, yet systematic design and cross-study translation remain hindered by heterogeneous experimental conditions, inconsistent reporting, and sparse structured data sets. Here, we develop a literature-derived data set of micro- and nanorobot systems and apply artificial intelligence (AI)-enabled structural meta-analysis to resolve relationships among bioengineering constraints, actuation strategies, environmental conditions, measurement practices, and performance. Dendrogram- and correlation-based clustering identifies three dominant descriptor modules spanning deployment context, actuation-measurement coupling, and design intent. Supervised learning reveals structured descriptor patterns in propulsion mechanisms, whereas prediction of instantaneous velocity remains only moderately generalizable, highlighting substantial domain shift across materials, propulsion physics, biological media, operating conditions, and measurement protocols. Overall, AI and machine learning provide a powerful framework for mapping heterogeneous design landscapes, exposing reporting biases and descriptor coupling, and advancing standardized data sets for reliable comparison and predictive design.

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Cite

BibTeXRIS
BibTeX
@article{rahimi2026machine,
  title = {{Machine Learning-Enabled Meta-Analysis of Bioengineering Micro- and Nanorobots.}},
  author = {Ehsan Rahimi and Mario Palacios-Corella and Mohammad Rahimi and Lucky Odirile Mohutsiwa and Josep Puigmartí-Luis and Salvador Pané},
  journal = {Nano letters},
  year = {2026},
  month = oct,
  doi = {10.1021/acs.nanolett.6c02258},
  url = {https://europepmc.org/article/MED/42917403},
  note = {CC BY},
}
RIS
TY  - JOUR
TI  - Machine Learning-Enabled Meta-Analysis of Bioengineering Micro- and Nanorobots.
AU  - Ehsan Rahimi
AU  - Mario Palacios-Corella
AU  - Mohammad Rahimi
AU  - Lucky Odirile Mohutsiwa
AU  - Josep Puigmartí-Luis
AU  - Salvador Pané
PY  - 2026
DA  - 2026/10/01
JO  - Nano letters
DO  - 10.1021/acs.nanolett.6c02258
UR  - https://europepmc.org/article/MED/42917403
AB  - Micro- and nanorobots are emerging bioengineering tools for targeted delivery, biosensing, imaging, and minimally invasive therapy, yet systematic design and cross-study translation remain hindered by heterogeneous experimental conditions, inconsistent reporting, and sparse structured data sets. Here, we develop a literature-derived data set of micro- and nanorobot systems and apply artificial intelligence (AI)-enabled structural meta-analysis to resolve relationships among bioengineering constraints, actuation strategies, environmental conditions, measurement practices, and performance. Dendrogram- and correlation-based clustering identifies three dominant descriptor modules spanning deployment context, actuation-measurement coupling, and design intent. Supervised learning reveals structured descriptor patterns in propulsion mechanisms, whereas prediction of instantaneous velocity remains only moderately generalizable, highlighting substantial domain shift across materials, propulsion physics, biological media, operating conditions, and measurement protocols. Overall, AI and machine learning provide a powerful framework for mapping heterogeneous design landscapes, exposing reporting biases and descriptor coupling, and advancing standardized data sets for reliable comparison and predictive design.
SN  - 1530-6984
SN  - 1530-6992
ER  -