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Analysis of institutional authors

Franco-Martinez, FAuthorLantada, AdCorresponding Author
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Artificial Intelligence Aided Design of Tissue Engineering Scaffolds Employing Virtual Tomography and 3D Convolutional Neural Networks

Publicated to:Materials. 14 (18): 5278- - 2021-09-01 14(18), DOI: 10.3390/ma14185278

Authors: Bermejillo Barrera, Maria Dolores; Franco-Martinez, Francisco; Diaz Lantada, Andres

Affiliations

Univ Politecn Madrid, ETSI Telecomunicat, Av Complutense 30, Madrid 28040, Spain - Author
Univ Politecn Madrid, Mech Engn Dept, ETSI Ind, Calle Jose Gutierrez Abascal 2, Madrid 28006, Spain - Author

Abstract

Design requirements for different mechanical metamaterials, porous constructions and lattice structures, employed as tissue engineering scaffolds, lead to multi-objective optimizations, due to the complex mechanical features of the biological tissues and structures they should mimic. In some cases, the use of conventional design and simulation methods for designing such tissue engineering scaffolds cannot be applied because of geometrical complexity, manufacturing defects or large aspect ratios leading to numerical mismatches. Artificial intelligence (AI) in general, and machine learning (ML) methods in particular, are already finding applications in tissue engineering and they can prove transformative resources for supporting designers in the field of regenerative medicine. In this study, the use of 3D convolutional neural networks (3D CNNs), trained using digital tomographies obtained from the CAD models, is validated as a powerful resource for predicting the mechanical properties of innovative scaffolds. The presented AI-aided or ML-aided design strategy is believed as an innovative approach in area of tissue engineering scaffolds, and of mechanical metamaterials in general. This strategy may lead to several applications beyond the tissue engineering field, as we analyze in the discussion and future proposals sections of the research study.

Keywords
3d convolutional neural networks (3d cnns)Artificial intelligence (ai)LearnMachine learning (ml)Materials discoveryTissue engineeringTissue engineering scaffolds

Quality index

Bibliometric impact. Analysis of the contribution and dissemination channel

The work has been published in the journal Materials due to its progression and the good impact it has achieved in recent years, according to the agency WoS (JCR), it has become a reference in its field. In the year of publication of the work, 2021, it was in position 18/79, thus managing to position itself as a Q1 (Primer Cuartil), in the category Metallurgy & Metallurgical Engineering.

From a relative perspective, and based on the normalized impact indicator calculated from World Citations provided by WoS (ESI, Clarivate), it yields a value for the citation normalization relative to the expected citation rate of: 1.28. This indicates that, compared to works in the same discipline and in the same year of publication, it ranks as a work cited above average. (source consulted: ESI Nov 14, 2024)

This information is reinforced by other indicators of the same type, which, although dynamic over time and dependent on the set of average global citations at the time of their calculation, consistently position the work at some point among the top 50% most cited in its field:

  • Weighted Average of Normalized Impact by the Scopus agency: 3.65 (source consulted: FECYT Feb 2024)
  • Field Citation Ratio (FCR) from Dimensions: 8.99 (source consulted: Dimensions May 2025)

Specifically, and according to different indexing agencies, this work has accumulated citations as of 2025-05-24, the following number of citations:

  • WoS: 32
  • Scopus: 50
  • Europe PMC: 8
  • OpenCitations: 38
Impact and social visibility

From the perspective of influence or social adoption, and based on metrics associated with mentions and interactions provided by agencies specializing in calculating the so-called "Alternative or Social Metrics," we can highlight as of 2025-05-24:

  • The use, from an academic perspective evidenced by the Altmetric agency indicator referring to aggregations made by the personal bibliographic manager Mendeley, gives us a total of: 130.
  • The use of this contribution in bookmarks, code forks, additions to favorite lists for recurrent reading, as well as general views, indicates that someone is using the publication as a basis for their current work. This may be a notable indicator of future more formal and academic citations. This claim is supported by the result of the "Capture" indicator, which yields a total of: 127 (PlumX).

With a more dissemination-oriented intent and targeting more general audiences, we can observe other more global scores such as:

  • The Total Score from Altmetric: 0.25.
  • The number of mentions on the social network X (formerly Twitter): 1 (Altmetric).

It is essential to present evidence supporting full alignment with institutional principles and guidelines on Open Science and the Conservation and Dissemination of Intellectual Heritage. A clear example of this is:

  • The work has been submitted to a journal whose editorial policy allows open Open Access publication.
Leadership analysis of institutional authors

There is a significant leadership presence as some of the institution’s authors appear as the first or last signer, detailed as follows: First Author (Barrera, MDB) and Last Author (DIAZ LANTADA, ANDRES).

the author responsible for correspondence tasks has been DIAZ LANTADA, ANDRES.