Thyroid Screening Techniques via Bioelectromagnetic Sensing: Imaging Models and Analytical and Computational Methods
Abstract
:1. Introduction
2. Models and Techniques
2.1. Bioelectromagnetic Models for Medical Imaging Applied to the Thyroid
2.1.1. Volumetric Models
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- Step 1: Multiple 2D images. Extraction of 2D images of the thyroid from multiple angles, as qualitatively depicted in Figure 3;
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- Step 2: Point correspondences. The point correspondences from multiple images are called tracks and are associated with 3D points of the thyroid. The computation of the tracks can be performed pairwise for two consecutive camera angles until all are fully described;
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- Step 3: Construction of the 3D model. The data are combined to create a 3D coordinate system, which leads to the complete 3D model of the thyroid.
2.1.2. Thermographic Models
2.1.3. Radiation Detection/Ablation Models
2.1.4. Model Attributes and Application Scope
2.1.5. Statistical Classification of the Related Bibliography
2.2. Models for the Analysis of Bioelectromagnetic Data
2.2.1. Analytical Methods
2.2.2. Computational Techniques
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- Step 1: Discretization. The mesh of the thyroid is constructed, as depicted in Figure 8;
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- Step 2: Selection of shape functions and formulation of the finite element equations. A pattern is selected for the distribution of the unknown variables, and the suitable governing laws are applied to the general equation of every finite element, i.e.,
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- Step 3: Assembly of the system of equations. With the equations of every finite element in the mesh appropriately derived, the global system of equations (modeling the entire problem) is constructed in the general form of
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- Step 5: Interpretation of the results. The solution of the system leads to an output, which can then be post-processed accordingly in order to calculate several quantities, gauges, or metrics of the model.
2.2.3. Artificial Intelligence and Machine Learning
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- Step 1: Data generation. The core mesh is generated through the use of widely accepted parameters, accompanied by the necessary statistical bounds, limits, or ranges, and its overall biomechanical behavior is then investigated with the FEM. In this manner and via the appropriate changes and adaptations, instead of the application of the regular computational method to the electromagnetic data, ML schemes can be used to study the behavior of demanding biomedical systems or even suggest means for the improvement of their statistical performance;
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- Step 2: Splitting the data. The data are split into three sets for training, validation, and testing. This step may be repeated to increase the robustness of the overall system and serve as a trustworthy tool for safe cross-validation;
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- Step 3: Model training. Using the datasets obtained in step 2, the model is trained according to the initial assumptions or physical conventions;
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- Step 4: Performance evaluation. The performance metrics primarily utilized in ML approaches are the mean square error and the mean absolute error, which refer to the difference between the calculated and reference quantities of the bioelectromagnetic problem.
2.2.4. Model Characteristics and Limitations
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- Complex system modeling: They can manipulate systems that are too complicated for any analytical solution—a fact that is very frequently encountered in biomedical problems.
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- Parametric studies: The parameters and conditions of the problem can easily be varied in order to investigate a broad range of cases and applications, which is basically impossible in the majority of biomedical problems.
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- Incorporation of theoretical frameworks: They can be employed to verify theoretical formulations by offering numerical solutions to various demanding cases.
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- Computational overhead: High-fidelity simulations can require considerable computational power, RAM, and CPU time, which may not always be available.
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- Dependence on models: They are frequently based on certain conventions and approximations, which can reduce their universality and yield errors.
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- Algorithmic limitations: Their efficiency can be restrained by the methods available, whose inherent artifacts (e.g., numerical dispersion, dissipation, and anisotropy errors or lack of convergence) can deteriorate the levels of accuracy.
3. Summary and Future Prospects
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Screening Technique | Operational Spectrum | Applications |
---|---|---|
Magnetic Resonance Imaging (MRI) | Radio frequency | Volumetric models and tissue property assessment |
Computer Tomography (CT) | X-ray | Volumetric models and tissue property assessment |
Positron Emission Tomography (PET) | Gamma rays | Volumetric models and tissue property assessment |
X-Ray | X-ray | Volumetric models and tissue property assessment |
Thermal Imaging | Infrared | Volumetric models and thermal mapping |
Device | Measured Parameter | Advantages | Disadvantages |
---|---|---|---|
Thermocouple | Voltage | Simplicity, durability, affordability, diversity, self-powered, wide temperature range | Non linearity, low voltage signal, lowest sensitivity, lowest stability reference |
Resistance temperature detector | Resistance | Accuracy, stability, linearity | Expensiveness, self heating, low output signal, low absolute resistance, current source required |
Thermistor | Resistance | High sensitivity, fast response time, high output signal, 2-wire Ohms measurement | Self heating, non linearity, narrow temperature range, current source required, fragility |
Liquid crystal sensor | Color change | Simplicity, durability, resistance sensitivity | Narrow temperature range, low response time, limited number of possible configurations |
Method | Source | Frequency | Objective |
---|---|---|---|
Volumetry | [32,34,35,36] | Infrared and X-ray | Reference and diagnostic |
Thermography | [32,37,42,43,44,45,46,47] | Infrared, microwave, and X-ray | Reference, diagnostic, and therapeutic |
Radiation | [25,27,28,50,51,52,53,54,55] | RF and microwave | Reference, diagnostic, and therapeutic |
Model | Applications | Advantages | Disadvantages |
---|---|---|---|
Analytical | Diagnostics and reference | Simplicity, speed, computational affordability, and generality | Not case-sensitive |
Computational | Diagnostics and reference | Accuracy and generality | Computationally expensive and slow |
Artificial Intelligence | Diagnostics | Case sensitivity, accuracy, and speed | Computational expensive and lack of generality |
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Varvari, A.A.; Pitilakis, A.; Karatzidis, D.I.; Kantartzis, N.V. Thyroid Screening Techniques via Bioelectromagnetic Sensing: Imaging Models and Analytical and Computational Methods. Sensors 2024, 24, 6104. https://doi.org/10.3390/s24186104
Varvari AA, Pitilakis A, Karatzidis DI, Kantartzis NV. Thyroid Screening Techniques via Bioelectromagnetic Sensing: Imaging Models and Analytical and Computational Methods. Sensors. 2024; 24(18):6104. https://doi.org/10.3390/s24186104
Chicago/Turabian StyleVarvari, Anna A., Alexandros Pitilakis, Dimitrios I. Karatzidis, and Nikolaos V. Kantartzis. 2024. "Thyroid Screening Techniques via Bioelectromagnetic Sensing: Imaging Models and Analytical and Computational Methods" Sensors 24, no. 18: 6104. https://doi.org/10.3390/s24186104
APA StyleVarvari, A. A., Pitilakis, A., Karatzidis, D. I., & Kantartzis, N. V. (2024). Thyroid Screening Techniques via Bioelectromagnetic Sensing: Imaging Models and Analytical and Computational Methods. Sensors, 24(18), 6104. https://doi.org/10.3390/s24186104