Bayesian machine learning optimization of microneedle design for biological fluid sampling

Tarar, Ceren; Aydın, Erdal; Yetisen, Ali K.; Tasoglu, Savas

doi:10.1039/d3sd00103b

Cited by 7 publications

(3 citation statements)

References 26 publications

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“…Machine learning (ML) is a subset of AI that focuses on the design of systems so that they can learn and make predictions based on previous experience that is called data. [ 23–30 ] The algorithm should first be trained on the data in the training set in which the parameters in the algorithm will be improved during the training process, and a machine learning model will be generated. It can then predict the results with new input data using the updated machine learning parameters.…”

Section: Methodsmentioning

confidence: 99%

Machine learning‐enabled optimization of melt electro‐writing three‐dimensional printing

Abdullah,

Ozarslan,

Farshi

et al. 2024

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Melt electrowriting (MEW) is a solvent‐free (i.e., no volatile chemicals), a high‐resolution three‐dimensional (3D) printing method that enables the fabrication of semi‐flexible structures with rigid polymers. Despite its advantages, the MEW process is sensitive to changes in printing parameters (e.g., voltage, printing pressure, and temperature), which can cause fluid column breakage, jet lag, and/or fiber pulsing, ultimately deteriorating the resolution and printing quality. In spite of the commonly used error‐and‐trial method to determine the most suitable parameters, here, we present a machine learning (ML)‐enabled image analysis‐based method for determining the optimum MEW printing parameters through an easy‐to‐use graphical user interface (GUI). We trained five different ML algorithms using 168 MEW 3D print samples, among which the Gaussian process regression ML model yielded 93% accuracy of the variability in the dependent variable, 0.12329 on root mean square error for the validation set and 0.015201 mean square error in predicting line thickness. Integration of ML with a control feedback loop and MEW can reduce the error‐and‐trial steps prior to the 3D printing process, decreasing the printing time (i.e., increasing the overall throughput of MEW) and material waste (i.e., improving the cost‐effectiveness of MEW). Moreover, embedding a trained ML model with the feedback control system in a GUI facilitates a more straightforward use of ML‐based optimization techniques in the industrial section (i.e., for users with no ML skills).

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Section: Methodsmentioning

confidence: 99%

Machine learning‐enabled optimization of melt electro‐writing three‐dimensional printing

Abdullah,

Ozarslan,

Farshi

et al. 2024

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“…WHO expects to present an upcoming global scarcity of healthcare excess by 2030 . AI-based designs can alleviate the workload of data interpretation or novel technology development in the fields of medical imaging, drug delivery, , and personalized medicine, − thus enhancing healthcare accessibility, affordability, and efficacy . Optics can enable breakthroughs in computational power such as quantum computers, communication, solar power generation, and medical diagnostics, paving the way for imagining systems, information, and data analysis .…”

Section: Introductionmentioning

confidence: 99%

AI-Based Metamaterial Design

Tezsezen,

Yigci,

Ahmadpour

et al. 2024

ACS Appl. Mater. Interfaces

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The use of metamaterials in various devices has revolutionized applications in optics, healthcare, acoustics, and power systems. Advancements in these fields demand novel or superior metamaterials that can demonstrate targeted control of electromagnetic, mechanical, and thermal properties of matter. Traditional design systems and methods often require manual manipulations which is time-consuming and resource intensive. The integration of artificial intelligence (AI) in optimizing metamaterial design can be employed to explore variant disciplines and address bottlenecks in design. AI-based metamaterial design can also enable the development of novel metamaterials by optimizing design parameters that cannot be achieved using traditional methods. The application of AI can be leveraged to accelerate the analysis of vast data sets as well as to better utilize limited data sets via generative models. This review covers the transformative impact of AI and AI-based metamaterial design for optics, acoustics, healthcare, and power systems. The current challenges, emerging fields, future directions, and bottlenecks within each domain are discussed.

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“…10,13 Numerical simulation of MNs has been widely used in the recent years. 22,54,55 An earlier study investigated the simulation of a MN array into the skin using LS-DYNA (ANSYS). 56 Their simulation consisted of four types of geometry (cone, taperedcone, beveled-tip, and pyramidal with a square base) for varied sizes of the model using polymer polylactic acid (PLA).…”

Section: ■ Introductionmentioning

confidence: 99%

Optimizing Solid Microneedle Design: A Comprehensive ML-Augmented DOE Approach

Abdullah,

Ahmadinejad,

Tasoglu

2024

ACS Meas. Sci. Au

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Microneedles (MNs), that is, a matrix of micrometer-scale needles, have diverse applications in drug delivery, skincare therapy, and health monitoring. MNs offer a minimally invasive alternative to hypodermic needles, characterized by rapid and painless procedures, cost-effective fabrication methods, and reduced tissue damage. This study explores four MN designs, cone-shaped, tapered cone-shaped, pyramidal with a square base, and pyramidal with a triangular-shaped base, and their optimization based on predefined criteria. The workflow encompasses three loading conditions: compressive load during insertion, critical buckling load, and bending loading resulting from incorrect insertion. Geometric parameters such as base radius/width, tip radius/width, height, and tapered angle tip influence the output criteria, namely, total deformation, critical buckling loads, factor of safety (FOS), and bending stress. The comprehensive framework employing a design of experiment approach within the ANSYS workbench toolbox establishes a mathematical model and a response surface fitting model. The resulting regression model, sensitivity chart, and response curve are used to create a multiobjective optimization problem that helps achieve an optimized MN geometrical design across the introduced four shapes, integrating machine learning (ML) techniques. This study contributes valuable insights into a potential ML-augmented optimization framework for MNs via needle designs to stay durable for various physiologically relevant conditions.

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Bayesian machine learning optimization of microneedle design for biological fluid sampling

Abstract: The deployment of microneedles in biological fluid sampling and drug delivery is an emerging field in biotechnology, which contributes greatly to minimally-invasive methods in medicine. Prior studies on microneedles proposed...

Cited by 7 publications

References 26 publications

Machine learning‐enabled optimization of melt electro‐writing three‐dimensional printing

Machine learning‐enabled optimization of melt electro‐writing three‐dimensional printing

AI-Based Metamaterial Design

Optimizing Solid Microneedle Design: A Comprehensive ML-Augmented DOE Approach

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