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Site-targeted drug delivery systems enhance therapeutic efficacy while minimizing systemic toxicity and treatment costs. Unlike conventional methods, these systems ensure precise drug delivery, improving bioavailability and reducing side effects. Targeted drug delivery is classified into three levels. First-order targeting directs drugs to the capillary beds of specific organs or tissues. Second-order targets specific cell types, such as tumor cells, using receptor-mediated interactions.
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Polymeric carriers enhance targeted drug delivery by increasing efficacy while minimizing off-target effects. These carriers comprise a biodegradable polymeric backbone integrated with functional elements that enable targeting, improve physicochemical properties, and regulate drug release.Targeting MechanismsThe targeting ability of polymeric carriers is mediated by a homing device, which is a molecular recognition component designed to selectively bind to specific tissues or cells. Monoclonal...
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Rate-programmed drug delivery systems release drugs in a controlled manner to maintain therapeutic levels. Three main designs include reservoir, matrix, and hybrid systems.Reservoir systems consist of a drug core enclosed within a membrane that controls drug release. In non-swelling reservoir systems, polymers like ethyl cellulose or polymethacrylates are used. These do not hydrate in aqueous media and control release through membrane thickness, porosity, or insolubility. This type includes...
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AI and Digital-Twin Synergy for Field Optimisation for Targeted Drug Delivery.

Robert Leonard Bernad1, Lăcrămioara Stoicu-Tivadar1, Mihaela Crişan-Vida1

  • 1Faculty of Automation and Computers, Politehnica University Timişoara, Romania.

Studies in Health Technology and Informatics
|May 23, 2026
PubMed
Summary
This summary is machine-generated.

This study introduces a fast, validated method for magnetic field mapping using AI and measurements. The FEMMAIOCR approach accelerates characterization for crucial biomedical applications.

Keywords:
Artificial IntelligenceFEMMMagnetic field mappingOCR teslametryPhysics Informed Neural Networkdigital twinoptimisation

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Area of Science:

  • Biomedical Engineering
  • Computational Physics
  • Artificial Intelligence

Background:

  • Precise magnetic field mapping is essential for advanced biomedical technologies.
  • Current methods can be time-consuming and computationally intensive.

Purpose of the Study:

  • To develop a rapid and experimentally validated method for magnetic field characterization.
  • To integrate Finite Element Method Magnetics (FEMM) simulations, AI, and optical character recognition (OCR) assisted measurements.

Main Methods:

  • Trained deep learning models (Volumetric Network, Physics-Informed Neural Network) using FEMM-simulated magnetization data.
  • Validated AI reconstructions against OCR-assisted teslametry measurements of a neodymium-iron-boron magnet.
  • Developed the FEMMAIOCR integrated approach.

Main Results:

  • AI-generated magnetic field reconstructions closely matched FEMM simulation results.
  • Achieved a computational speedup exceeding fifty times compared to traditional FEMM simulations.
  • OCR measurements confirmed the accuracy of the AI-driven approach within experimental uncertainty.

Conclusions:

  • The FEMMAIOCR approach offers a significant advancement in magnetic field characterization speed and accuracy.
  • This method is highly suitable for applications in magnetic drug targeting, microrobotics, and other biomagnetic devices.
  • Provides a validated, efficient tool for characterizing magnetic fields in complex biomedical systems.