The integration of artificial intelligence (AI) in healthcare, particularly in digital cytology, has the potential to enhance diagnostic accuracy and workflow efficiency. However, AI adoption remains limited due to technological and human-related barriers. Understanding the perceptions and experiences of healthcare professionals is essential for overcoming these challenges and facilitating effective AI implementation.

This study aimed to assess AI integration in digital cytology workflows by evaluating professionals' perspectives on its benefits, challenges, and requirements for successful adoption.

A survey was conducted among 150 professionals working in public and private healthcare settings in Italy, including laboratory technicians (35%), medical doctors (25%), biologists (20%), and specialists in diagnostic technical sciences (20%). Data were collected through a structured Computer-Assisted Web Interview (CAWI) and a Virtual Focus Group (VFG) to capture quantitative and qualitative insights on AI familiarity, perceived advantages, and barriers to adoption.

The findings indicated varying levels of AI familiarity among professionals. While many recognized AI's potential to improve diagnostic accuracy and streamline workflows, concerns were raised regarding resistance to change, implementation costs, and doubts about AI reliability. Participants emphasized the need for structured training and continuous support to facilitate AI adoption in digital cytology.

Addressing barriers such as resistance, cost, and trust is essential for the successful integration of AI in digital cytology workflows. Tailored training programs and ongoing professional support can enhance AI adoption, ultimately optimizing diagnostic processes and improving clinical outcomes.

This study aimed to evaluate the applicability of deep learning technology to thyroid ultrasound images for classification of thyroid nodules.

This retrospective analysis included ultrasound images of patients with thyroid nodules investigated by fine-needle aspiration at the thyroid clinic of a single center from April 2010 to September 2012. Thyroid nodules with cytopathologic results of Bethesda category V (suspicious for malignancy) or VI (malignant) were defined as thyroid cancer. Multiple deep learning algorithms based on convolutional neural networks (CNNs) -ResNet, DenseNet, and EfficientNet-were utilized, and Siamese neural networks facilitated multi-view analysis of paired transverse and longitudinal ultrasound images.

Among 1,048 analyzed thyroid nodules from 943 patients, 306 (29%) were identified as thyroid cancer. In a subgroup analysis of transverse and longitudinal images, longitudinal images showed superior prediction ability. Multi-view modeling, based on paired transverse and longitudinal images, significantly improved the model performance; with an accuracy of 0.82 (95% confidence intervals [CI], 0.80 to 0.86) with ResNet50, 0.83 (95% CI, 0.83 to 0.88) with DenseNet201, and 0.81 (95% CI, 0.79 to 0.84) with EfficientNetv2_ s. Training with high-resolution images obtained using the latest equipment tended to improve model performance in association with increased sensitivity.

CNN algorithms applied to ultrasound images demonstrated substantial accuracy in thyroid nodule classification, indicating their potential as valuable tools for diagnosing thyroid cancer. However, in real-world clinical settings, it is important to aware that model performance may vary depending on the quality of images acquired by different physicians and imaging devices.

To evaluate the performance of GPT-4 and GPT-4o in accurately identifying features and categories from thyroid nodule ultrasound images following the American College of Radiology Thyroid Imaging Reporting and Data System (TI-RADS).

This comparative validation study, conducted between October 2023 and May 2024, utilized 202 thyroid ultrasound images sourced from 3 open-access databases. Both complete and cropped versions of each image were independently evaluated by expert radiologists to establish a reference standard for TI-RADS features and categories. GPT-4 and GPT-4o were prompted to analyze each image, and their generated TI-RADS outputs were compared to the reference standard.

GPT-4 demonstrated high specificity but low sensitivity when assessing complete thyroid ultrasound images across most TI-RADS categories, resulting in mixed overall accuracy. For low-risk nodules (benign), GPT-4 achieved 25.0% sensitivity, 99.5% specificity, and 93.6% accuracy. In contrast, in the higher risk moderately suspicious category GPT-4 showed 75% sensitivity, 22.2% specificity, and 42.1% accuracy. While GPT-4 effectively identified features like smooth margins (73% vs 65% the reference standard), it struggled to identify other features like isoechoic echogenicity (5% vs 46%), and echogenic foci (3% vs 27%). The assessment of cropped images using both GPT-4 and GPT-4o followed similar patterns, though with slight deviations indicating a decrease in performance compared to GPT-4's assessment of complete images.

While GPT-4 and GPT-4o models show potential for improving the efficiency of thyroid nodule triage, their performance remains suboptimal, particularly in higher-risk categories. Further refinement and validation of these models are necessary before clinical implementation.

The integration of artificial intelligence (AI) into ultrasound medicine has revolutionized medical imaging, enhancing diagnostic accuracy and clinical workflows. This review focuses on the applications, challenges, and future directions of AI technologies, particularly machine learning (ML) and its subset, deep learning (DL), in ultrasound diagnostics. By leveraging advanced algorithms such as convolutional neural networks (CNNs), AI has significantly improved image acquisition, quality assessment, and objective disease diagnosis. AI-driven solutions now facilitate automated image analysis, intelligent diagnostic assistance, and medical education, enabling precise lesion detection across various organs while reducing physician workload. AI's error detection capabilities further enhance diagnostic accuracy. Looking ahead, the integration of AI with ultrasound is expected to deepen, promoting trends in standardization, personalized treatment, and intelligent healthcare, particularly in underserved areas. Despite its potential, comprehensive assessments of AI's diagnostic accuracy and ethical implications remain limited, necessitating rigorous evaluations to ensure effectiveness in clinical practice. This review provides a systematic evaluation of AI technologies in ultrasound medicine, highlighting their transformative potential to improve global healthcare outcomes.

This study explores a self-learning method as an auxiliary approach in residency training for distinguishing between benign and malignant thyroid nodules.

Conducted from March to December 2022, internal medicine residents underwent three repeated learning sessions with a "learning set" comprising 3000 thyroid nodule images. Diagnostic performances for internal medicine residents were assessed before the study, after every learning session, and for radiology residents before and after one-on-one education, using a "test set," comprising 120 thyroid nodule images. Finally, all residents repeated the same test using artificial intelligence computer-assisted diagnosis (AI-CAD).

Twenty-one internal medicine and eight radiology residents participated. Initially, internal medicine residents had a lower area under the receiver operating characteristic curve (AUROC) than radiology residents (0.578 vs. 0.701, P < 0.001), improving post-learning (0.578 to 0.709, P < 0.001) to a comparable level with radiology residents (0.709 vs. 0.735, P = 0.17). Further improvement occurred with AI-CAD for both group (0.709 to 0.755, P < 0.001; 0.735 to 0.768, P = 0.03).

The proposed iterative self-learning method using a large volume of ultrasonographic images can assist beginners, such as residents, in thyroid imaging to differentiate benign and malignant thyroid nodules. Additionally, AI-CAD can improve the diagnostic performance across varied levels of experience in thyroid imaging.

For some common thyroid-related conditions with high prevalence and long follow-up times, ChatGPT can be used to respond to common thyroid-related questions.

In this cross-sectional study, we assessed the ability of ChatGPT (version GPT-4.0) to provide accurate, comprehensive, compassionate, and satisfactory responses to common thyroid-related questions.

First, we obtained 28 thyroid-related questions from the Huayitong app, which together with the 2 interfering questions eventually formed 30 questions. Then, these questions were responded to by ChatGPT (on July 19, 2023), a junior specialist, and a senior specialist (on July 20, 2023) separately. Finally, 26 patients and 11 thyroid surgeons evaluated those responses on 4 dimensions: accuracy, comprehensiveness, compassion, and satisfaction.

Among the 30 questions and responses, ChatGPT's speed of response was faster than that of the junior specialist (8.69 [7.53-9.48] vs 4.33 [4.05-4.60]; P < .001) and the senior specialist (8.69 [7.53-9.48] vs 4.22 [3.36-4.76]; P < .001). The word count of the ChatGPT's responses was greater than that of both the junior specialist (341.50 [301.00-384.25] vs 74.50 [51.75-84.75]; P < .001) and senior specialist (341.50 [301.00-384.25] vs 104.00 [63.75-177.75]; P < .001). ChatGPT received higher scores than the junior specialist and senior specialist in terms of accuracy, comprehensiveness, compassion, and satisfaction in responding to common thyroid-related questions.

ChatGPT performed better than a junior specialist and senior specialist in answering common thyroid-related questions, but further research is needed to validate the logical ability of the ChatGPT for complex thyroid questions.

Background/Objectives: Generative artificial intelligence (AI) such as ChatGPT has developed rapidly in recent years, and in the medical field, its usefulness for diagnostic assistance has been reported. However, there are few reports of AI use in dental fields. Methods: We created 20 questions that we had encountered in clinical pediatric dentistry, and collected the responses to these questions from three types of generative AI. The responses were evaluated on a 5-point scale by six pediatric dental specialists using the Global Quality Scale. Results: The average scores were >3 for the three types of generated AI tools that we tested; the overall average was 3.34. Although the responses for questions related to "consultations from guardians" or "systemic diseases" had high scores (>3.5), the score for questions related to "dental abnormalities" was 2.99, which was the lowest among the four categories. Conclusions: Our results show the usefulness of generative AI tools in clinical pediatric dentistry, indicating that these tools will be useful assistants in the dental field.

Thyroid nodules, with a prevalence of almost 25% in the general population, are a common occurrence. Their prevalence varies considerably depending on demographics such as age and sex as well as the presence of risk factors. This article provides a comprehensive overview of the prevalence, risk stratification and current management strategies for thyroid nodules, with a particular focus on changes in diagnostic and therapeutic protocols that have occurred over the past 10 years. Several sonography-based stratification systems (such as Thyroid Imaging Reporting and Data Systems (TIRADS)) might help to predict the malignancy risk of nodules, potentially eliminating the need for biopsy in many instances. However, large or suspicious nodules necessitate cytological evaluation following fine-needle aspiration biopsy for accurate classification. In the case of cytology yielding indeterminate results, additional tools, such as molecular testing, can assist in guiding the management plan. Surgery is no longer the only treatment for symptomatic or malignant nodules: active surveillance or local ablative treatments might be beneficial for appropriately selected patients. To enhance clinician-patient interactions and discussions about diagnostic options, shared decision-making tools have been developed. A personalized, risk-based protocol promotes high-quality care while minimizing costs and unnecessary testing.

The integration of artificial intelligence (AI) in cytopathology is an emerging field with transformative potential, aiming to enhance diagnostic precision and operational efficiency. This umbrella review seeks to identify prevailing themes, opportunities, challenges, and recommendations related to AI in cytopathology. Utilizing a standardized checklist and quality control procedures, this review examines recent advancements and future implications of AI technologies in this domain. Twenty-one review studies were selected through a systematic process. AI has demonstrated promise in automating and refining diagnostic processes, potentially reducing errors and improving patient outcomes. However, several critical challenges need to be addressed to realize the benefits of AI fully. This review underscores the necessity for rigorous validation, ongoing empirical data on diagnostic accuracy, standardized protocols, and effective integration with existing clinical workflows. Ethical issues, including data privacy and algorithmic bias, must be managed to ensure responsible AI applications. Additionally, high costs and substantial training requirements present barriers to widespread AI adoption. Future directions highlight the importance of applying successful integration strategies from histopathology and radiology to cytopathology. Continuous research is needed to improve model interpretability, validation, and standardization. Developing effective strategies for incorporating AI into clinical practice and establishing comprehensive ethical and regulatory frameworks will be crucial for overcoming these challenges. In conclusion, while AI holds significant promise for advancing cytopathology, its full potential can only be achieved by addressing challenges related to validation, cost, and ethics. This review provides an overview of current advancements, identifies ongoing challenges, and offers a roadmap for the successful integration of AI into diagnostic cytopathology, informed by insights from related fields.

To evaluate the efficacy of combining predictive artificial intelligence (AI) and image similarity model to risk stratify thyroid nodules, using retrospective external validation study.

Two datasets were used to determine efficacy of the AI application. One was Stanford dataset ultrasound images of 192 nodules between April 2017 and May 2018 and the second was private practice consisting of 118 thyroid nodule images between January 2018 and December 2023. The nodules had definitive diagnosis by cytology or surgical pathology. The AI application was used to predict the diagnosis and American College of Radiology Thyroid Imaging and Data System (ACR TI-RADS) score.

In the Stanford dataset, the AI application predicted malignancies with sensitivity of 1.0 and specificity of 0.55. Positive predictive value (PPV) was 0.18 and negative predictive value (NPV) was 1.0. The Area Under the Curve - Receiver Operating Characteristic was 0.78. ACR TI-RADS based clinical recommendation had a polychoric correlation of 0.67. In the private dataset, the AI application predicted malignancies with sensitivity of 0.91 and specificity of 0.95. PPV was 0.8 and NPV was 0.98. The area under the curve - receiver operating characteristic was 0.93 and accuracy was 0.94. ACR TI-RADS based score had a polychoric correlation of 0.94.

The AI application showed good performance for sensitivity and NPV between the two datasets and demonstrated potential for 61.5% reduction in the need for fine needle aspiration and strong correlation to ACR TI-RADS. However, PPV was variable between the datasets possibly from variability in image selection and prevalence of malignancy. If implemented widely and consistently among various clinical settings, this could lead to decreased patient burden associated with an invasive procedure and possibly to decreased health care spending.

Use of artificial intelligence (AI) to predict clinical outcomes in thyroid nodule diagnostics has grown exponentially over the past decade. The greatest challenge is in understanding the best model to apply to one's own patient population, and how to operationalize such a model in practice.

A literature search of PubMed and IEEE Xplore was conducted for English-language publications between January 1, 2015 and January 1, 2023, studying diagnostic tests on suspected thyroid nodules that used AI. We excluded articles without prospective or external validation, nonprimary literature, duplicates, focused on nonnodular thyroid conditions, not using AI, and those incidentally using AI in support of an experimental diagnostic outside standard clinical practice. Quality was graded by Oxford level of evidence.

A total of 61 studies were identified; all performed external validation, 16 studies were prospective, and 33 compared a model to physician prediction of ground truth. Statistical validation was reported in 50 papers. A diagnostic pipeline was abstracted, yielding 5 high-level outcomes: (1) nodule localization, (2) ultrasound (US) risk score, (3) molecular status, (4) malignancy, and (5) long-term prognosis. Seven prospective studies validated a single commercial AI; strengths included automating nodule feature assessment from US and assisting the physician in predicting malignancy risk, while weaknesses included automated margin prediction and interobserver variability.

Models predominantly used US images to predict malignancy. Of 4 Food and Drug Administration-approved products, only S-Detect was extensively validated. Implementing an AI model locally requires data sanitization and revalidation to ensure appropriate clinical performance.

Thyroid cancer (TC) is a common endocrine malignancy with an increasing incidence worldwide. Early diagnosis is particularly important for TC patients, because it allows patients to receive treatment as early as possible. Artificial intelligence (AI) provides great advantages for complex healthcare systems by analyzing big data based on machine learning. Nowadays, AI is widely used in the early diagnosis of cancer such as TC. Ultrasound detection and fine needle aspiration biopsy are the main methods for early diagnosis of TC. AI has been widely used in the detection of malignancy in thyroid nodules by ultrasound images, cytopathology images and molecular markers. It shows great potential in auxiliary medical diagnosis. The latest clinical trial has shown that the performance of AI models matches with the diagnostic efficiency of experienced clinicians, and more efficient AI tools will be developed in the future. Therefore, in this review, we summarized the recent advances in the application of AI algorithms in assessing the risk of malignancy in thyroid nodules. The objective of this review was to provide a data base for the clinical use of AI-assisted diagnosis in TC, as well as to provide new ideas for the next generation of AI-assisted diagnosis in TC.

Background: This study aimed to evaluate the clinical impact of an artificial intelligence (AI)-based decision support system (DSS), Koios DS, on the analysis of ultrasound imaging and suspicious characteristics for thyroid nodule risk stratification. Methods: A retrospective ultrasound study was conducted on all thyroid nodules with histological findings from June 2021 to December 2022 in a thyroid nodule clinic. The diagnostic performance of ultrasound imaging was evaluated by six readers on the American College of Radiology Thyroid Imaging Reporting and Data System (ACR TI-RADS) before and after the use of the AI-based DSS and by AI itself. Results: A total of 172 patients (83.1% women) with a mean age of 52.3 ± 15.3 years were evaluated. The mean maximum nodular diameter was 2.9 ± 1.2 cm, with 11.0% being differentiated thyroid carcinomas. Among the nodules initially classified as ACR TI-RADS 3 and 4, AI reclassified 81.4% and 24.5% into lower risk categories, respectively. Receiver operating characteristic (ROC) curve analysis was performed to evaluate the diagnostic performance of the readers and the AI-based DSS versus histological diagnosis. There was an increase in the area under the ROC curve (AUROC) after the use of AI (0.776 vs. 0.817, p < 0.001). The AI-based DSS improved the mean sensitivity (Sens) (82.3% vs. 86.5%) and specificity (Spe) (38.3% vs. 54.8%), produced a high negative predictive value (94.5% vs. 96.4%), and increased the positive predictive value (PPV) (14.0% vs. 16.1%) and diagnostic precision (43.0% vs. 49.3%). Based on the ACR TI-RADS score, there was significant improvement in interobserver agreement after the use of AI (r = 0.741 for ultrasound imaging alone vs. 0.981 for ultrasound imaging and the AI-based DSS, p < 0.001). Conclusions: The use of an AI-based DSS was associated with overall improvement in the diagnostic efficacy of ultrasound imaging, based on the AUROC, as well as an increase in Sens, Spe, negative and PPVs, and diagnostic accuracy. There was also a reduction in interobserver variability and an increase in the degree of concordance with the use of AI. AI reclassified more than half of the nodules with intermediate ACR TI-RADS scores into lower risk categories.

This study aimed to evaluate the rates of necessary diagnostic and therapeutic procedures in a cohort of patients undergoing their first endocrine consultation for thyroid nodular disease.

This was an observational study conducted between January 1 and June 30, 2023, on patients undergoing their first endocrine consultation for thyroid nodular disease. Data were collected, including age, thyroid-stimulating hormone (TSH) concentration, reasons for performing thyroid ultrasound (US), and thyroid US reports. The US was performed at the time of the endocrine consultation according to the American College of Radiology Thyroid Imaging, Reporting and Data System (ACR-TIRADS) risk stratification system. Patients underwent further investigations/treatment or simple US follow-up as necessary.

A total of 373 patients with thyroid nodules were evaluated. Reasons for undergoing thyroid US were unrelated to suspected thyroid disease in 126 (33.8%), incidentalomas in 91 (24.4%), dysfunction or positivity for thyroid antibodies in 67 (18%), symptoms or visible nodules in 61 (16.3%), and family history of thyroid disease in 28 (7.5%). A total of 193 diagnostic or therapeutic procedures were performed in 133 (35.7%) patients [fine-needle aspiration (FNA): 121 (62.7%), surgery: 28 (14.5%), percutaneous ethanol injection: 20 (10.4%), scintigraphy: 10 (5.2%); thermal ablation: 7 (3.6%), and radioactive treatment: 7 (3.6%)].

In the present study only one-third of the patients undergoing endocrine consultation with first detected thyroid nodules required any diagnostic or therapeutic procedures.

To explore the feasibility of combined radiomics of post-treatment I-131 total body scan (TBS) and clinical parameter to predict successful ablation in low-risk papillary thyroid carcinoma (PTC) patients. Data of low-risk PTC patients who underwent total/near total thyroidectomy and I-131 ablation 30 mCi between April 2015 and July 2021 were retrospectively reviewed. The clinical factors studied included age, sex, and pre-ablative serum thyroglobulin (Tg). Radiomic features were extracted via PyRadiomics, and radiomic feature selection was performed. The predictive performance for successful ablation of the clinical parameter, radiomic, and combined models (radiomics combined with clinical parameter) was calculated using the area under the receiver operating characteristic curve (AUC). One hundred and thirty patients were included. Successful ablation was achieved in 77 patients (59.2%). The mean pre-ablative Tg in the unsuccessful group (15.50 ± 18.04 ng/ml) was statistically significantly higher than those in the successful ablation group (7.12 ± 7.15 ng/ml). The clinical parameter, radiomic, and combined models produced AUCs of 0.66, 0.77, and 0.87 in the training sets, and 0.65, 0.69, and 0.78 in the validation sets, respectively. The combined model produced a significantly higher AUC than that of the clinical parameter (p < 0.05). Radiomic analysis of the post-treatment TBS combined with pre-ablative serum Tg showed a significant improvement in the predictive performance of successful ablation in low-risk PTC patients compared to the use of clinical parameter alone.Thai Clinical Trials Registry TCTR identification number is TCTR20230816004 ( https://www.thaiclinicaltrials.org/show/TCTR20230816004 ).

Ultrasound (US) of the thyroid has been used as a diagnostic tool since the late 1960s. US is the most important imaging tool for diagnosing thyroid disease. In the majority of cases a correct diagnosis can already be made in synopsis of the sonographic together with clinical findings and basal thyroid hormone parameters. However, the characterization of thyroid nodules by US remains challenging. The introduction of Thyroid Imaging Reporting and Data Systems (TIRADSs) has improved diagnostic accuracy of thyroid cancer significantly. Newer techniques such as elastography, superb microvascular imaging (SMI), contrast enhanced ultrasound (CEUS) and multiparametric ultrasound (MPUS) expand diagnostic options and tools further. In addition, the use of artificial intelligence (AI) is a promising tool to improve and simplify diagnostics of thyroid nodules and there is evidence that AI can exceed the performance of humans. Combining different US techniques with the introduction of new software, the use of AI, FNB as well as molecular markers might pave the way for a completely new area of diagnostic accuracy in thyroid disease. Finally, interventional ultrasound using US-guided thermal ablation (TA) procedures are increasingly proposed as therapy options for benign as well as malignant thyroid diseases.

Considerable heterogeneity is observed in the malignancy rates of thyroid nodules classified as category 4 according to the Thyroid Imaging Reporting and Data System (TI-RADS). This study was aimed at comparing the diagnostic performance of artificial intelligence algorithms and radiologists with different experience levels in distinguishing benign and malignant TI-RADS 4 (TR4) nodules.

Between January 2019 and September 2022, 1117 TR4 nodules with well-defined pathological findings were collected for this retrospective study. An independent external data set of 125 TR4 nodules was incorporated for testing purposes. Traditional feature-based machine learning (ML) models, deep convolutional neural networks (DCNN) models and a fusion model that integrated the prediction outcomes from all models were used to classify benign and malignant TR4 nodules. A fivefold cross-validation approach was employed, and the diagnostic performance of each model and radiologists was compared.

In the external test data set, the area under the receiver operating characteristic curve (AUROC) of the three DCNN-based secondary transfer learning models-InceptionV3, DenseNet121 and ResNet50-were 0.852, 0.837 and 0.856, respectively. These values were higher than those of the three traditional ML models-logistic regression, multilayer perceptron and random forest-at 0.782, 0.790, and 0.767, respectively, and higher than that of an experienced radiologist (0.815). The fusion diagnostic model we developed, with an AUROC of 0.880, was found to outperform the experienced radiologist in diagnosing TR4 nodules.

The integration of artificial intelligence algorithms into medical imaging studies could improve the accuracy of identifying high-risk TR4 nodules pre-operatively and have significant clinical application potential.

Multiple US-based systems for risk stratification of thyroid nodules are in use worldwide. Unfortunately, the malignancy probability assigned to a nodule varies, and terms and definitions are not consistent, leading to confusion and making it challenging to compare study results and craft revisions. Consistent application of these systems is further hampered by interobserver variability in identifying the sonographic features on which they are founded. In 2018, an international multidisciplinary group of 19 physicians with expertise in thyroid sonography (termed the International Thyroid Nodule Ultrasound Working Group) was convened with the goal of developing an international system, tentatively called the International Thyroid Imaging Reporting and Data System, or I-TIRADS, in two phases: (phase I) creation of a lexicon and atlas of US descriptors of thyroid nodules and (phase II) development of a system that estimates the malignancy risk of a thyroid nodule. This article presents the methods and results of phase I. The purpose herein is to show what has been accomplished thus far, as well as generate interest in and support for this effort in the global thyroid community.

Ultrasound is the first-line imaging modality for detection and classification of thyroid nodules. Certain features observable by ultrasound have recently been equated with potential malignancy. This retrospective cohort study was conducted to test the hypothesis that radiomics of the four categorical divisions (medullary [MTC], papillary [PTC], or follicular [FTC] carcinoma and follicular thyroid adenoma [FTA]) demonstrate distinctive sonographic characteristics. Using an artificial neural network model for proof of concept, these sonographic features served as input.

A total of 148 patients were enrolled for study, all with confirmed thyroid pathology in one of the four named categories. Preoperative ultrasound profiles were obtained via standardized protocols. The neural network consisted of seven input neurons; three hidden layers with 50, 250, and 100 neurons, respectively; and one output layer.

Radiomics of contour, structure, and calcifications differed significantly according to nodule type (p = 0.025, p = 0.032, and p = 0.0002, respectively). Levels of accuracy shown by artificial neural network analysis in discriminating among categories ranged from 0.59 to 0.98 (95% confidence interval [CI]: 0.57-0.99), with positive and negative predictive ranges of 0.41-0.99 and 0.78-0.97, respectively.

Our data indicate that some MTCs, PTCs, FTCs, and FTAs have distinctive sonographic characteristics. However, a significant overlap of these characteristics may impede an explicit classification. Further prospective investigations involving larger patient and nodule numbers and multicenter access should be pursued to determine if neural networks of this sort are beneficial, helping to classify neoplasms of the thyroid gland.