On the possibility of using temperature to aid in thyroid nodule investigation

Thyroid Screening Techniques via Bioelectromagnetic Sensing: Imaging Models and Analytical and Computational Methods

The thyroid gland, which is sensitive to electromagnetic radiation, plays a crucial role in the regulation of the hormonal levels of the human body. Biosensors, on the other hand, are essential to access information and derive metrics about the condition of the thyroid by means of of non-invasive techniques. This paper provides a systematic overview of the recent literature on bioelectromagnetic models and methods designed specifically for the study of the thyroid. The survey, which was conducted within the scope of the radiation transmitter-thyroid model-sensor system, is centered around the following three primary axes: the bands of the frequency spectrum taken into account, the design of the model, and the methodology and/or algorithm. Our review highlights the areas of specialization and underscores the limitations of each model, including its time, memory, and resource requirements, as well as its performance. In this manner, this specific work may offer guidance throughout the selection process of a bioelectromagnetic model of the thyroid, as well as a technique for its analysis based on the available resources and the specific parameters of the electromagnetic problem under consideration.

Dual-Tree Complex Wavelet Pooling and Attention-Based Modified U-Net Architecture for Automated Breast Thermogram Segmentation and Classification

Thermography is a non-invasive and non-contact method for detecting cancer in its initial stages by examining the temperature variation between both breasts. Preprocessing methods such as resizing, ROI (region of interest) segmentation, and augmentation are frequently used to enhance the accuracy of breast thermogram analysis. In this study, a modified U-Net architecture (DTCWAU-Net) that uses dual-tree complex wavelet transform (DTCWT) and attention gate for breast thermal image segmentation for frontal and lateral view thermograms, aiming to outline ROI for potential tumor detection, was proposed. The proposed approach achieved an average Dice coefficient of 93.03% and a sensitivity of 94.82%, showcasing its potential for accurate breast thermogram segmentation. Classification of breast thermograms into healthy or cancerous categories was carried out by extracting texture- and histogram-based features and deep features from segmented thermograms. Feature selection was performed using Neighborhood Component Analysis (NCA), followed by the application of machine learning classifiers. When compared to other state-of-the-art approaches for detecting breast cancer using a thermogram, the proposed methodology showed a higher accuracy of 99.90% for VGG16 deep features with NCA and Random Forest classifier. Simulation results expound that the proposed method can be used in breast cancer screening, facilitating early detection, and enhancing treatment outcomes.

Enhanced thyroid nodule segmentation through U-Net and VGG16 fusion with feature engineering: A comprehensive study

BACKGROUND AND OBJECTIVE

The thyroid gland, a key component of the endocrine system, is pivotal in regulating bodily functions. Thermography, a non-invasive imaging technique utilizing infrared cameras, has emerged as a diagnostic tool for thyroid-related conditions, offering advantages such as early detection and risk stratification. Artificial intelligence (AI) has demonstrated success in medical diagnostics, and its integration into thermal imaging analysis holds promise for improving diagnostic capabilities. This study aims to explore the potential of AI, specifically convolutional neural networks (CNNs), in enhancing the analysis of thyroid thermograms for the detection of nodules and abnormalities.

METHODS

Artificial intelligence (AI) and machine learning techniques are integrated to enhance thyroid thermal image analysis. Specifically, a fusion of U-Net and VGG16, combined with feature engineering (FE), is proposed for accurate thyroid nodule segmentation. The novelty of this research lies in leveraging feature engineering in transfer learning for the segmentation of thyroid nodules, even in the presence of a limited dataset.

RESULTS

The study presents results from four conducted studies, demonstrating the efficacy of this approach even with a limited dataset. It's observed that in study 4, using FE has led to a significant improvement in the value of the dice coefficient. Even for the small size of the masked region, incorporating radiomics with FE resulted in significant improvements in the segmentation dice coefficient. It's promising that one can achieve higher dice coefficients by employing different models and refining them.

CONCLUSION

The findings here underscore the potential of AI for precise and efficient segmentation of thyroid nodules, paving the way for improved thyroid health assessment.

Model-Optimizing Radiofrequency Parameters of 3D Finite Element Analysis for Ablation of Benign Thyroid Nodules

Radiofrequency (RF) ablation represents an efficient strategy to reduce the volume of thyroid nodules. In this study, a finite element model was developed with the aim of optimizing RF parameters, e.g., input power and treatment duration, in order to achieve the target volume reduction rate (VRR) for a thyroid nodule. RF ablation is modelled as a coupled electro-thermal problem wherein the electric field is applied to induce tissue heating. The electric problem is solved with the Laplace equation, the temperature distribution is estimated with the Pennes bioheat equation, and the thermal damage is evaluated using the Arrhenius equation. The optimization model is applied to RF electrode with different active tip lengths in the interval from 5 mm to 40 mm at the 5 mm step. For each case, we also explored the influence of tumour blood perfusion rate on RF ablation outcomes. The model highlights that longer active tips are more efficient as they require lesser power and shorter treatment time to reach the target VRR. Moreover, this condition is characterized by a reduced transversal ablation zone. In addition, a higher blood perfusion increases the heat dispersion, requiring a different combination of RF power and time treatment to achieve the target VRR. The model may contribute to an improvement in patient-specific RF ablation treatment.

A New Technique for Localization of Parathyroid Adenoma: Infrared Thermal Scanning of the Neck

Many radiological techniques are used to locate the adenoma preoperatively in cases of primary hyperparathyroidism, but the location of many adenomas still cannot be detected. Since adenomas are hypervascular lesions, their temperature is high. Infrared thermal scanning can reveal local temperature differences in hypervascular lesions. The location of the adenoma could not be determined by preoperative radiological examinations in a 58-year-old male patient who was scheduled for surgery with the diagnosis of primary hyperparathyroidism. By infrared thermal scanning, a nearly 2°F higher temperature was measured in the inferior of the right thyroid lobe compared to the other perithyroidal regions. During the exploration, the adenoma was found at this point and removed. Infrared thermal scanning of the neck is promising as a new technique that can be used both preoperatively and intraoperatively to locate the adenoma in primary hyperparathyroidism cases.

Combining Genetic Algorithms and SVM for Breast Cancer Diagnosis Using Infrared Thermography

Breast cancer is one of the leading causes of mortality globally, but early diagnosis and treatment can increase the cancer survival rate. In this context, thermography is a suitable approach to help early diagnosis due to the temperature difference between cancerous tissues and healthy neighboring tissues. This work proposes an ensemble method for selecting models and features by combining a Genetic Algorithm (GA) and the Support Vector Machine (SVM) classifier to diagnose breast cancer. Our evaluation demonstrates that the approach presents a significant contribution to the early diagnosis of breast cancer, presenting results with 94.79% Area Under the Receiver Operating Characteristic Curve and 97.18% of Accuracy.

A Computational Study on the Role of Parameters for Identification of Thyroid Nodules by Infrared Images (and Comparison with Real Data)

According to experts and medical literature, healthy thyroids and thyroids containing benign nodules tend to be less inflamed and less active than those with malignant nodules. It seems to be a consensus that malignant nodules have more blood veins and more blood circulation. This may be related to the maintenance of the nodule's heat at a higher level compared with neighboring tissues. If the internal heat modifies the skin radiation, then it could be detected by infrared sensors. The goal of this work is the investigation of the factors that allow this detection, and the possible relation with any pattern referent to nodule malignancy. We aim to consider a wide range of factors, so a great number of numerical simulations of the heat transfer in the region under analysis, based on the Finite Element method, are performed to study the influence of each nodule and patient characteristics on the infrared sensor acquisition. To do so, the protocol for infrared thyroid examination used in our university's hospital is simulated in the numerical study. This protocol presents two phases. In the first one, the body under observation is in steady state. In the second one, it is submitted to thermal stress (transient state). Both are simulated in order to verify if it is possible (by infrared sensors) to identify different behavior referent to malignant nodules. Moreover, when the simulation indicates possible important aspects, patients with and without similar characteristics are examined to confirm such influences. The results show that the tissues between skin and thyroid, as well as the nodule size, have an influence on superficial temperatures. Other thermal parameters of thyroid nodules show little influence on surface infrared emissions, for instance, those related to the vascularization of the nodule. All details of the physical parameters used in the simulations, characteristics of the real nodules and thermal examinations are publicly available, allowing these simulations to be compared with other types of heat transfer solutions and infrared examination protocols. Among the main contributions of this work, we highlight the simulation of the possible range of parameters, and definition of the simulation approach for mapping the used infrared protocol, promoting the investigation of a possible relation between the heat transfer process and the data obtained by infrared acquisitions.