Artificial Neural Networks - Methodological Advances and Biomedical Applications

Artificial intelligence (AI) has emerged as a powerful set of tools for engineering innovative materials. However, the AI-aided design of materials textures has not yet been researched in depth. In order to explore the potentials of AI for discovering innovative biointerfaces and engineering materials surfaces, especially for biomedical applications, this study focuses on the control of wettability through design-controlled hierarchical surfaces, whose design is supported and its performance predicted thanks to adequately structured and trained artificial neural networks (ANN). The authors explain the creation of a comprehensive library of microtextured surfaces with well-known wettability properties. Such a library is processed and employed for the generation and training of artificial neural networks, which can predict the actual wetting performance of new design biointerfaces. The present research demonstrates that AI can importantly support the engineering of innovative hierarchical or multiscale surfaces when complex-to-model properties and phenomena, such as wettability and wetting, are involved.

Download Full-text

Applied Artificial Neural Networks: from Associative Memories to Biomedical Applications

Artificial Neural Networks - Methodological Advances and Biomedical Applications ◽

10.5772/15325 ◽

2011 ◽

Author(s):

Mahmood Amiri ◽

Katayoun Derakhshandeh

Keyword(s):

Neural Networks ◽

Artificial Neural Networks ◽

Biomedical Applications ◽

Associative Memories ◽

Artificial Neural

Download Full-text

Comparative Evaluation of Artificial Neural Networks and Data Analysis in Predicting Liposome Size in a Periodic Disturbance Micromixer

Micromachines ◽

10.3390/mi12101164 ◽

2021 ◽

Vol 12 (10) ◽

pp. 1164

Author(s):

Ixchel Ocampo ◽

Rubén R. López ◽

Sergio Camacho-León ◽

Vahé Nerguizian ◽

Ion Stiharu

Keyword(s):

Neural Networks ◽

Artificial Neural Networks ◽

Data Analysis ◽

Flow Rate ◽

Biomedical Applications ◽

Periodic Disturbance ◽

Relevant Variables ◽

Total Data ◽

Liposome Size ◽

Artificial Neural

Artificial neural networks (ANN) and data analysis (DA) are powerful tools for supporting decision-making. They are employed in diverse fields, and one of them is nanotechnology; for example, in predicting silver nanoparticles size. To our knowledge, we are the first to use ANN to predict liposome size (LZ). Liposomes are lipid nanoparticles used in different biomedical applications that can be produced in Dean-Forces-based microdevices such as the Periodic Disturbance Micromixer (PDM). In this work, ANN and DA techniques are used to build a LZ prediction model by using the most relevant variables in a PDM, the Flow Rate Radio (FRR), and the Total Flow Rate (TFR), and the temperature, solvents, and concentrations were kept constant. The ANN was designed in MATLAB and fed data from 60 experiments with 70% training, 15% validation, and 15% testing. For DA, a regression analysis was used. The model was evaluated; it showed a 0.98147 correlation coefficient for training and 0.97247 in total data compared with 0.882 obtained by DA.

Download Full-text