Detection Of Thyroid Nodules Research Articles

Sonographic risk stratification of FDG-avid thyroid nodules using the Thyroid Imaging Reporting and Data System.

The increasing usage of positron emission tomography/computed tomography (PET/CT) for detection and monitoring of malignancy has led to an increase in incidental detection of thyroid nodules. Nodules that demonstrate increased avidity for 2-[18F]-fluoro-2-deoxy-d-glucose (FDG) have been shown to carry a high incidence of malignancy and warrant further investigation. At present, there has been limited research on the risk stratification of FDG-avid thyroid incidentalomas. Thus, this study aims to evaluate the efficacy of the ACR TIRADS classification in the risk stratification of such nodules. Data were collected retrospectively for FDG-avid thyroid incidentalomas over a 10-year period. Nodules were characterised using the TIRADS classification and, subsequently, underwent fine-needle aspirate cytology. Cytological findings were classified using the Bethesda reporting system. Non-diagnostic samples (Bethesda class I) were excluded. The remaining samples were divided into two groups: benign (Bethesda class II) or suspicious for malignancy/malignant (Bethesda class III or above). Thirty-six percent of low-risk nodules and 45% of high-risk nodules were malignant, respectively (P = 0.516). The sensitivity and specificity of TIRADS for detection of malignant nodules were 56% and 54%, respectively. There were no malignant TIRADS 1 or 2 nodules. The absence of any suspicious sonographic features had a 1.0 negative predictive value. FDG-avid nodules classified as TIRADS 1 or 2 or have no suspicious ultrasound features have a 0% incidence of malignancy and thus may not require further assessment with fine-needle aspirate cytology (FNA) when detected incidentally. FDG-avid nodules that are TIRADS 3 or above should undergo FNA regardless of size due to the high risk of malignancy and poor sensitivity of the TIRADS classification system.

Hybrid deep learning assisted multi classification: Grading of malignant thyroid nodules.

Thyroid nodules are commonly diagnosed with ultrasonography, which includes internal characteristics, varying looks, and hazy boundaries, making it challenging for a clinician to differentiate between malignant and benign forms based only on visual identification. The advancement of AI, particularly DL, provides significant breakthroughs in the domain of medical image identification. Yet, there are certain obstacles to achieving accuracy as well as efficacy in thyroid nodule detection. The thyroid nodules in this study are detected and classified using an inventive hybrid deep learning-assisted multi-classification method. The median blur method is applied in this work to eliminate the salt and pepper noise from the image. Then MPIU-Net-based segmentation is utilized to segment the image. The LGBPNP-based features are retrieved from the segmented image to obtain a single histogram sequence of the LGBP pattern in addition to other features like extraction of multi-texton and LTP-based features. After the feature extraction, the data augmentation process is applied and then the features are fed to the hybrid classification-based nodule classification model that comprises Deep Maxout and CNN, this hybrid classification trains the features and predicts the thyroid nodule. Additionally, the TIRADS score classification is used for the projected malignant thyroid nodule coupled with statistical features collected from the segmented. The DBNAAF with transfer learning model is employed to classify the grading of malignant thyroid nodules, where the weights of the model are learned with transfer learning. The MCC of the Hybrid Model is 0.9445, whereas the DCNN is 0.6858, YOLOV3-DMRF is 0.7229, CNN is 0.7780, DBN is 0.7601, Bi-GRU is 0.7038, Deep Maxout is 0.7528, and RNN is 0.8522, respectively.

A fully autonomous robotic ultrasound system for thyroid scanning

The current thyroid ultrasound relies heavily on the experience and skills of the sonographer and the expertise of the radiologist, and the process is physically and cognitively exhausting. In this paper, we report a fully autonomous robotic ultrasound system, which is able to scan thyroid regions without human assistance and identify malignant nod- ules. In this system, human skeleton point recognition, reinforcement learning, and force feedback are used to deal with the difficulties in locating thyroid targets. The orientation of the ultrasound probe is adjusted dynamically via Bayesian optimization. Experimental results on human participants demonstrated that this system can perform high-quality ultrasound scans, close to manual scans obtained by clinicians. Additionally, it has the potential to detect thyroid nodules and provide data on nodule characteristics for American College of Radiology Thyroid Imaging Reporting and Data System (ACR TI-RADS) calculation.

Machine learning to predict the occurrence of thyroid nodules: towards a quantitative approach for judicious utilization of thyroid ultrasonography.

Ultrasound is instrumental in the early detection of thyroid nodules, which is crucial for appropriate management and favorable outcomes. However, there is a lack of clinical guidelines for the judicious use of thyroid ultrasonography in routine screening. Machine learning (ML) has been increasingly used on big data to predict clinical outcomes. This study aims to leverage the ML approach in assessing the risk of thyroid nodules based on common clinical features. Data were sourced from a Chinese cohort undergoing routine physical examinations including thyroid ultrasonography between 2013 and 2023. Models were established to predict the 3-year risk of thyroid nodules based on patients' baseline characteristics and laboratory tests. Four ML algorithms, including logistic regression, random forest, extreme gradient boosting, and light gradient boosting machine, were trained and tested using fivefold cross-validation. The importance of each feature was measured by the permutation score. A nomogram was established to facilitate risk assessment in the clinical settings. The final dataset comprised 4,386 eligible subjects. Thyroid nodules were detected in 54.8% (n=2,404) individuals within the 3-year observation period. All ML models significantly outperformed the baseline regression model, successfully predicting the occurrence of thyroid nodules in approximately two-thirds of individuals. Age, high-density lipoprotein, fasting blood glucose and creatinine levels exhibited the highest impact on the outcome in these models. The nomogram showed consistency and validity, providing greater net benefits for clinical decision-making than other strategies. This study demonstrates the viability of an ML-based approach in predicting the occurrence of thyroid nodules. The findings highlight the potential of ML models in identifying high-risk individuals for personalized screening, thereby guiding the judicious use of ultrasound in this context.

Semi-Supervised Thyroid Nodule Detection in Ultrasound Videos.

Deep learning techniques have been investigated for the computer-aided diagnosis of thyroid nodules in ultrasound images. However, most existing thyroid nodule detection methods were simply based on static ultrasound images, which cannot well explore spatial and temporal information following the clinical examination process. In this paper, we propose a novel video-based semi-supervised framework for ultrasound thyroid nodule detection. Especially, considering clinical examinations that need to detect thyroid nodules at the ultrasonic probe positions, we first construct an adjacent frame guided detection backbone network by using adjacent supporting reference frames. To further reduce the labour-intensive thyroid nodule annotation in ultrasound videos, we extend the video-based detection in a semi-supervised manner by using both labeled and unlabeled videos. Based on the detection consistency in sequential neighbouring frames, a pseudo label adaptation strategy is proposed for the refinement of unpredicted frames. The proposed framework is validated on 996 transverse viewed and 1088 longitudinal viewed ultrasound videos. Experimental results demonstrated the superior performance of our proposed method in the ultrasound video-based detection of thyroid nodules.

Establishment and preliminary application of personalized three-dimensional reconstruction of thyroid gland with automatic detection of thyroid nodules based on ultrasound videos.

A well display of the spatial location of thyroid nodules in the thyroid is important for surgical path planning and surgeon-patient communication. The aim of this study was to establish a three-dimensional (3D) reconstruction method of the thyroid gland, thyroid nodule, and carotid artery with automatic detection based on two-dimensional (2D) ultrasound videos, and to evaluate its clinical value. Ultrasound videos, including the thyroid gland with nodule, isthmus of thyroid gland, and ipsilateral carotid artery, were recorded. BC-UNet, MTN-Net, and RDPA-U-Net network models were innovatively employed for segmentation of the thyroid glands, the thyroid nodules, and the carotid artery respectively. Marching Cubes algorithm was used for reconstruction, while Laplacian smoothing algorithm was employed to smooth the 3D model surface. Using this model, 20 patients and 15 surgeons completed surveys on the effectiveness of this model for the pre-surgery demonstration of nodule location as well as surgeon-patient communication. The thyroid gland with nodule, isthmus of gland, and carotid artery were reconstructed and displayed. With the 3D model, the understanding of the spatial location of thyroid nodules improved in all three surgeon groups, eliminating the influence of professional levels. In the patient survey, the patients' understanding of the thyroid nodule location and procedure for surgery were significantly improved. In addition, with the 3D model, the time for doctors to explain to patients was significantly reduced (16.75 vs. 8.85min, p=0.001). To our knowledge, this is the first report of constructing a 3D thyroid model using a deep learning technique for personalized thyroid segmentation based on 2D ultrasound videos. The preliminary clinical application showed that it was conducive to the comprehension of the location of thyroid nodules for surgeons and patients, with significant improvement on the efficiency of surgeon-patient communication.

Effect of Music Therapy on Pain During Thyroid Fine Needle Aspiration Biopsy; A Randomized Controlled Clinical Trial

IntroductionHigh-resolution ultrasonography devices have led to the increased detection of thyroid nodules and the need for fine-needle aspiration biopsy (FNAB). However, FNAB is an invasive procedure that can cause discomfort and pain. Music therapy has been used for centuries to alleviate pain, and our clinical trial was conducted to investigate its impact on pain scores during thyroid FNAB. Materials and methodsWe conducted a randomized, controlled clinical trial, including adult patients undergoing thyroid FNAB. We evaluated the nonpharmacological intervention of listening to music before and during thyroid FNAB. We used a slow, nonlyrical, flowing melody with a tempo of 60 to 80 beats per minute as a therapeutic intervention for managing anxiety and pain. It had low tones, minimal percussion, and a volume of around 60 dB. The Beck Anxiety Inventory and Visual Pain Scale were used. ResultsThe study included 529 patients assigned to either the music group (n = 258, 48.7%) or the control group (n = 271, 51.2%). The patients were categorized into subgroups based on either a single nodule (73.63%) or multiple nodules (24.18%). When all patients or single nodule and multiple data are examined separately, the intervention group showed statistically significantly lower pain scores than the control group. ConclusionOur study is among the first on this topic and the largest in the available literature to demonstrate that listening to music during FNAB significantly reduces pain and anxiety. Music therapy is an effective, safe, and noninvasive intervention that can improve patient care and reduce distress and pain.

Effects of Overweight on Risk of Thyroid Nodules in Children and Adolescents: The Fukushima Health Management Survey.

Examining how overweight/obesity impacts thyroid nodule development in children and adolescents by sex and age can speculate on the mechanism. We examined whether overweight in children and adolescents are associated with thyroid nodule development by sex and age. Approximately 300,000 participants who underwent thyroid ultrasonography in the Fukushima Health Management Survey after a nuclear accident were enrolled. Those without nodules in the initial two examinations (1-3 and 4-5 years postaccident) were prospectively assessed for nodule development in the third examination (6-7 years postaccident) relative to baseline overweight status, with an average follow-up of 4.2 years. A population-based prospective cohort study. The first and second thyroid examinations involved 299,939 and 237,691 participants, respectively, excluding those with thyroid nodules. After the third examination, 184,519 participants were finalized for analysis. Multivariable-adjusted odds ratios of new detected thyroid nodules for overweight participants compared with normal-weight participants. New thyroid nodules were detected in 660 participants. Being overweight was positively associated with thyroid nodules. The adjusted odds ratio (95% confidence interval) of thyroid nodules for overweight participants compared with other participants was 1.27 (1.04-1.57). Additionally, the multivariable-adjusted odds ratios for males and females with overweight were 1.21 and 1.32, respectively, and those for different age groups (0-9, 10-14, and 15-19 years) ranged from 1.17 to 1.75. Being overweight was associated with thyroid nodules in children and adolescents, mostly adolescent females, regardless of their proximity to the nuclear power plant.

Automatic detection of thyroid nodules with a real-time artificial intelligence system in a real clinical scenario and the associated influencing factors.

At present, most articles mainly focused on the diagnosis of thyroid nodules by using artificial intelligence (AI), and there was little research on the detection performance of AI in thyroid nodules. To explore the value of a real-time AI based on computer-aided diagnosis system in the detection of thyroid nodules and to analyze the factors influencing the detection accuracy. From June 1, 2022 to December 31, 2023, 224 consecutive patients with 587 thyroid nodules were prospective collected. Based on the detection results determined by two experienced radiologists (both with more than 15 years experience in thyroid diagnosis), the detection ability of thyroid nodules of radiologists with different experience levels (junior radiologist with 1 year experience and senior radiologist with 5 years experience in thyroid diagnosis) and real-time AI were compared. According to the logistic regression analysis, the factors influencing the real-time AI detection of thyroid nodules were analyzed. The detection rate of thyroid nodules by real-time AI was significantly higher than that of junior radiologist (P = 0.013), but lower than that of senior radiologist (P = 0.001). Multivariate logistic regression analysis showed that nodules size, superior pole, outside (near carotid artery), close to vessel, echogenicity (isoechoic, hyperechoic, mixed-echoic), morphology (not very regular, irregular), margin (unclear), ACR TI-RADS category 4 and 5 were significant independent influencing factors (all P < 0.05). With the combination of real-time AI and radiologists, junior and senior radiologist increased the detection rate to 97.4% (P < 0.001) and 99.1% (P = 0.015) respectively. The real-time AI has good performance in thyroid nodule detection and can be a good auxiliary tool in the clinical work of radiologists.

Sensitivity and Specificity Analysis of Fine Needle Aspiration Cytology (FNAC) for Thyroid Swelling.

Introduction Thyroid gland disorders, such as goiters or tumor masses, are the result of localized or systemic aberrations of the thyroid gland. The purpose of this research was to see how effective fine needle aspiration cytology (FNAC) was in detecting thyroid swelling in patients with thyroid swelling. It is critical to be able to differentiate between benign and malignant thyroid nodules to reduce unnecessary thyroid surgeries. It is hypothesized that FNAC is not a reliable diagnostic tool to detect thyroid nodules in patients with thyroid swelling and that there is a significant variation between the number of thyroid nodules detected by FNAC and the number of thyroid nodules that were eventually diagnosed as malignant by surgery. The significance of this research shows the effectiveness of diagnostic tests for thyroid nodules in patients with thyroid swelling can help reduce unnecessary surgeries and improve patient care. Methods This cross-sectional study was carried out at the Department of Pathology at a tertiary care hospital in central India, from March 1, 2022 to June 31, 2022. Fifty patients with thyroid swelling were covered in the study. All patients were chosen after an ultrasound revealed goiter on clinical grounds. Results The majority of patients in the age group 31-40 years (33.33%) were female (74.67%). About 54.67% of the patients had a single thyroid nodule. FNAC had a sensitivity of 95.38%, a specificity of 53.33%, a positive predictive value of 86.67% and a negative predictive value of 88.57%. Conclusion FNAC is a simple, economical, and commonly used first-line diagnostic method for thyroid cancer. A false negative or false positive cytological diagnosis may be produced as a result of thyroid cytology. The study emphasizes the need to improve basic healthcare in rural India by treating FNAC as a first-line diagnostic test for thyroid swellings to guide management, although it does not replace histopathology.

Residual U-Net approach for thyroid nodule detection and classification from thyroid ultrasound images

With so many thyroid knobs (nodules) discovered by accident, it is critical to recognize as many aberrant knobs (nodules) as possible from fine-needle aspiration (FNA) biopsies or other medical procedures while excluding those that are virtually certainly benign. Thyroid ultrasonography, on the other hand, is prone to interobserver variability and subjective translations. An effective deep learning model for segmenting and categorizing thyroid nodules in this study follows the stages below: data collection from a well-known archive, The Thyroid Digital Image Database (TDID), which comprises ultrasound pictures from 298 patients, preprocessing using anisotropic diffusion filter (ADF) for removing noise and enhancing the images, segmentation using a bilateral filter for segmenting images, feature extraction using grey level occurrence matrix (GLCM), feature selection using Multi-objective Particle Swarm with Random Forest Optimization (MbPSRA) and finally classification happens were Residual U-Net will be used. Experiment evaluation states the proposed model outperforms well than other state-of-art models.

Integrated use of contrast-enhanced and grey-scale ultrasound in assigning American College of Radiology Thyroid Imaging-Reporting and Data System scores for characterisation of thyroid nodules: A prospective observational study.

The advent and increased use of high-resolution ultrasonography has resulted in improved detection of thyroid nodules. Even with the use of various Thyroid Imaging-Reporting and Data System, accurate imaging diagnosis of malignant thyroid nodules has been suboptimal, which necessitated use of newer modalities like contrast-enhanced ultrasonography alone and in combination for this purpose. Although the combined use of various Thyroid Imaging-Reporting and Data System and contrast-enhanced ultrasonography has turned out to be accurate in many studies, the ideal way to integrate contrast-enhanced ultrasonography into the Thyroid Imaging-Reporting and Data System algorithm is under-investigated. To estimate and compare the diagnostic accuracy of American College of Radiology Thyroid Imaging-Reporting and Data System and contrast-enhanced ultrasonography in differentiating benign and malignant nodules alone and in combination. To estimate the diagnostic accuracy of contrast-enhanced ultrasonography in re-categorisation of Thyroid Imaging-Reporting and Data System 3 and Thyroid Imaging-Reporting and Data System 4 thyroid nodules. This was a prospective cohort study performed in a tertiary care university-based hospital for 3 years. Adult patients with clinical or previous sonographic diagnosis of thyroid nodules were selected. Each of the nodules were assessed using ultrasonography and categorised using American College of Radiology Thyroid Imaging-Reporting and Data System criteria. The lesion was then assessed for contrast-enhanced ultrasonography features. The final diagnosis of the nodules was made using fine needle aspiration cytology. The diagnostic accuracy in diagnosis of malignant thyroid nodules for each of the American College of Radiology Thyroid Imaging-Reporting and Data System and contrast-enhanced ultrasonography alone and in combination was assessed. The diagnostic accuracy of contrast-enhanced ultrasonography in diagnosis of malignant thyroid nodules categorised as Thyroid Imaging-Reporting and Data System 3 and Thyroid Imaging-Reporting and Data System 4 was also assessed. American College of Radiology Thyroid Imaging-Reporting and Data System had a sensitivity, specificity, negative predictive value, positive predictive value and diagnostic accuracy of 86.6%, 54.5%, 17.4%, 97.3% and 57.7%, respectively, in diagnosis of malignant thyroid nodules. Contrast-enhanced ultrasonography had a sensitivity, specificity, negative predictive value, positive predictive value and diagnostic accuracy of 86.6%, 95.4%, 67.9%, 98.4% and 94.4%, respectively, in diagnosis of malignant thyroid nodules. Contrast-enhanced ultrasonography had sensitivity, specificity, negative predictive value, positive predictive value and diagnostic accuracy of 93.3%, 100.0%, 100.0%, 99.2% and 99.3%, respectively, in re-categorisation of Thyroid Imaging-Reporting and Data System 3 and Thyroid Imaging-Reporting and Data System 4 nodules. Contrast-enhanced ultrasonography can play a key role in diagnosis of malignant thyroid nodules which are categorised as indeterminate on grey-scale ultrasound.

Identification method of thyroid nodule ultrasonography based on self-supervised learning dual-branch attention learning framework.

Thyroid ultrasound is a widely used diagnostic technique for thyroid nodules in clinical practice. However, due to the characteristics of ultrasonic imaging, such as low image contrast, high noise levels, and heterogeneous features, detecting and identifying nodules remains challenging. In addition, high-quality labeled medical imaging datasets are rare, and thyroid ultrasound images are no exception, posing a significant challenge for machine learning applications in medical image analysis. In this study, we propose a Dual-branch Attention Learning (DBAL) convolutional neural network framework to enhance thyroid nodule detection by capturing contextual information. Leveraging jigsaw puzzles as a pretext task during network training, we improve the network's generalization ability with limited data. Our framework effectively captures intrinsic features in a global-to-local manner. Experimental results involve self-supervised pre-training on unlabeled ultrasound images and fine-tuning using 1216 clinical ultrasound images from a collaborating hospital. DBAL achieves accurate discrimination of thyroid nodules, with a 88.5% correct diagnosis rate for malignant and benign nodules and a 93.7% area under the ROC curve. This novel approach demonstrates promising potential in clinical applications for its accuracy and efficiency.

A multitask approach for automated detection and segmentation of thyroid nodules in ultrasound images

An increase in the incidence and diagnosis of thyroid nodules and thyroid cancer underscores the need for a better approach to nodule detection and risk stratification in ultrasound (US) images that can reduce healthcare costs, patient discomfort, and unnecessary invasive procedures. However, variability in ultrasound technique and interpretation makes the diagnostic process partially subjective. Therefore, an automated approach that detects and segments nodules could improve performance on downstream tasks, such as risk stratification. Ultrasound studies were acquired from 280 patients at UCLA Health, totaling 9888 images, and annotated by collaborating radiologists. Current deep learning architectures for segmentation are typically semi-automated because they are evaluated solely on images known to have nodules and do not assess ability to identify suspicious images. However, the proposed multitask approach both detects suspicious images and segments potential nodules; this allows for a clinically translatable model that aptly parallels the workflow for thyroid nodule assessment. The multitask approach is centered on an anomaly detection (AD) module that can be integrated with any UNet architecture variant to improve image-level nodule detection. Of the evaluated multitask models, a UNet with a ImageNet pretrained encoder and AD achieved the highest F1 score of 0.839 and image-wide Dice similarity coefficient of 0.808 on the hold-out test set. Furthermore, models were evaluated on two external validations datasets to demonstrate generalizability and robustness to data variability. Ultimately, the proposed architecture is an automated multitask method that expands on previous methods by successfully both detecting and segmenting nodules in ultrasound.

Role of Genetic Testing in the Management of Indeterminate Thyroid Nodules in the Indian Setting.

The increased detection of thyroid nodules in the human population has led to an increase in the number of thyroid surgeries without an improvement in survival outcomes. Though the choice for surgery is straightforward in malignant thyroid nodules, the decision is far more complex in those nodules that get categorized into indeterminate thyroid nodules (ITN) by fine needle aspiration. Therefore, there is a pressing need to develop a tool that will aid in decision-making among the ITN. In this context, the development of various molecular testing (MT) panels has helped to confirm or rule out malignancy, reducing unnecessary surgeries and potentially guiding the extent of surgery as well. Currently, such tests are widely used among the Western population but these MT panels are not used by the South Asian population because of non-availability of validated panels and the high cost involved. There is a need to develop a suitable panel which is population-specific and validate the same. In this review, we would focus on current trends in the management of ITN among the South Asian population and how to develop a novel MT panel which is cost-effective, with high diagnostic accuracy obviating the need for expensive panels that already exist.

ThyroidNet: A Deep Learning Network for Localization and Classification of Thyroid Nodules.

This study aims to establish an artificial intelligence model, ThyroidNet, to diagnose thyroid nodules using deep learning techniques accurately. A novel method, ThyroidNet, is introduced and evaluated based on deep learning for the localization and classification of thyroid nodules. First, we propose the multitask TransUnet, which combines the TransUnet encoder and decoder with multitask learning. Second, we propose the DualLoss function, tailored to the thyroid nodule localization and classification tasks. It balances the learning of the localization and classification tasks to help improve the model's generalization ability. Third, we introduce strategies for augmenting the data. Finally, we submit a novel deep learning model, ThyroidNet, to accurately detect thyroid nodules. ThyroidNet was evaluated on private datasets and was comparable to other existing methods, including U-Net and TransUnet. Experimental results show that ThyroidNet outperformed these methods in localizing and classifying thyroid nodules. It achieved improved accuracy of 3.9% and 1.5%, respectively. ThyroidNet significantly improves the clinical diagnosis of thyroid nodules and supports medical image analysis tasks. Future research directions include optimization of the model structure, expansion of the dataset size, reduction of computational complexity and memory requirements, and exploration of additional applications of ThyroidNet in medical image analysis.

Comparison of artificial intelligence, elastic imaging, and the thyroid imaging reporting and data system in the differential diagnosis of suspicious nodules.

Ultrasound is widely used for detecting thyroid nodules in clinical practice. This retrospective study aimed to assess the diagnostic efficacy of the American College of Radiology Thyroid Imaging Reporting and Data System (ACR-TIRADS), S-Detect, and elastography of the carotid artery for suspicious thyroid nodules and to determine the complementary value of artificial intelligence and elastography. Between January 2021 and November 2021, 101 consecutive patients with 138 thyroid nodules were enrolled in The First Hospital of China Medical University. All nodules were evaluated using ACR-TIRADS categories (TR), S-Detect, and elastography, and then the diagnostic performance of the different methods and the combined assessment were compared. The inclusion criteria were the following: (I) TR3, TR4, and TR5 nodules, which were defined as "suspicious nodules"; (II) patients who had surgical or cytopathological results after ultrasound examination; and (III) voluntary enrollment in this study. Meanwhile, the exclusion criteria were the following: (I) TR1 and TR2 nodules, (II) patients who had undergone fine-needle aspiration before ultrasound examination, and (III) inconclusive cytologic findings. A total of 71 patients (12 men and 59 women) with 94 suspicious thyroid nodules (42 benign nodules and 52 malignant nodules) were finally included in this study. S-Detect had a significantly better sensitivity than did ACR-TIRADS [S-Detect: 98.1%, 95% confidence interval (CI): 89.7-100.0%; ACR-TIRADS: 84.6%, 95% CI: 71.9-93.1%; P=0.036], but its specificity was much lower (S-Detect: 19.0%; 95% CI: 8.6-34.1%; ACR-TIRADS: 40.5%, 95% CI: 25.6-56.7%; P=0.032). The accuracy was not significantly different between S-Detect (62.8%; 95% CI: 52.2-72.5%) and ACR-TIRADS (64.9%; 95% CI: 54.4-74.5%) (P=0.761). The elasticity contrast index (ECI) was not definitively useful in identifying suspicious thyroid nodules (P=0.592). Compared with the use of ACR-TIRADS and S-Detect alone, the specificity (45.2%; 95% CI: 29.8-61.3%), positive predictive value (65.2%; 95% CI: 52.4-76.5%), accuracy (66.0%; 95% CI: 55.5-75.4%), and the area under the receiver operating characteristic curve (0.640; 95% CI: 0.534-0.736) of their combination were higher but not significantly so. At present, S-Detect cannot replace manual diagnosis, and the value of elastography of the carotid artery in diagnosing suspected thyroid nodules remains unclear.

Automatic Thyroid Nodule Detection in Ultrasound Imaging With Improved YOLOv5 Neural Network

Automatic Thyroid Nodule Detection in Ultrasound Imaging With Improved YOLOv5 Neural Network

Comparison of Thyroglobulin and Thyroid Function in Pregnant Women between Counties with a Median Urinary Iodine Concentration of 100–149 μg/L and 150–249 μg/L

ObjectiveThis study explored whether thyroglobulin and thyroid disease prevalence rates were higher in pregnant Chinese women with a median urinary iodine concentration of 100–149 μg/L, compared with those with a median urinary iodine concentration of 150–249 μg/L maintained through sustainable universal salt iodization. MethodsThis was a cross-sectional study in which 812 healthy pregnant women were enrolled to collect samples of their household edible salt, urine, and blood during their routine antenatal care in the 18 counties in Fujian Province, China. The levels of salt iodine concentration, urinary iodine concentration (UIC), free triiodothyronine (FT3), free thyroid hormone (FT4), thyroid-stimulating hormone (TSH), thyroglobulin (Tg), thyroid peroxidase antibody and thyroglobulin antibody were assessed during the routine antenatal care visits. ResultsThe median UIC (mUIC) in pregnant women was 130.8 μg/L (interquartile range = 91.5–198.1 μg/L) in the counties with an mUIC of 100–149 μg/L (Group I), and 172.0 μg/L (interquartile range = 123.5–244.4 μg/L) in the counties with an mUIC of 150–249 μg/L (Group II). Goiter prevalence and thyroid nodule detection rates showed no difference between Group I and Group II (P > 0.05). Except for FT4 values, the TSH, FT4, FT3, Tg and Tg values > 40 (μg/L) and the thyroid diseases prevalence rate (TDR) showed no significant differences between Group I and Group II (P > 0.05), whether or not iodine supplementation measures were taken. ConclusionCompared with an mUIC of 150–249 μg/L, not only there was no difference in thyroid morphology, but also the Tg value, rate of Tg values > 40 μg/L, and TDR were not higher in pregnant women in the counties with an mUIC of 100–149 μg/L achieved through sustainable universal salt iodization in Fujian Province, China.

An IoT and Deep Learning-Based Smart Healthcare Framework for Thyroid Cancer Detection

A world of healthcare possibilities has been opened with the development of the Internet of Medical Things and related machine learning, deep learning, and artificial intelligence approaches. It has a broad range of uses: when linked to the Internet, common medical equipment and sensors may gather important data; deep learning and artificial intelligence algorithms use this data to understand symptoms and patterns and allow remote healthcare. There are a large number of people affected by thyroid disorders across the world. The ultrasound-based thyroid nodule detection using traditional methods increased the burden on the expertise. Therefore, alternate methods are required to overcome this problem. In order to facilitate early thyroid disorder detection, this research aims to offer an IoT-based ensemble learning framework. In the proposed ensemble model, three pre-trained models DeiT, Mixer-MLP and Swin Transformer, are used for feature extraction. The mRMR technique is used for relevant feature selection. A total of 24 machine learning models have been trained, and weighted average ensemble learning is employed using the Improved Jaya optimization algorithm and Coronavirus Herd Immunity optimization algorithm. The ensemble model with the improved Jaya optimization algorithm achieved excellent results. The best value for accuracy, precision, sensitivity, specificity, F2-score and ROC-AUC score are 92.83%, 87.76%, 97.66%, 88.89%, 0.9551 and 0.9357, respectively. The main focus of this research is to increase the specificity. A poor value of specificity can lead to a high false positive rate. This situation can increase anxiety and emotionally weaken the patient. The proposed ensemble model with the Improved Jaya optimization algorithm outperformed state-of-the-art techniques and can assist medical experts.