Machine learning (ML) is an interdisciplinary sector in the subset of artificial intelligence (AI) that creates systems to set up logical connections using algorithms, and thus offers predictions for complex data analysis. In the present review, an up-to-date summary of the current state of the art regarding ML and AI implementation for thyroid nodule ultrasound characterization and cancer is provided, highlighting controversies over AI application as well as possible benefits of ML, such as, for example, training purposes. There is evidence that AI increases diagnostic accuracy and significantly limits inter-observer variability by using standardized mathematical algorithms. It could also be of aid in practice settings with limited sub-specialty expertise, offering a second opinion by means of radiomics and computer-assisted diagnosis. The introduction of AI represents a revolutionary event in thyroid nodule evaluation, but key issues for further implementation include integration with radiologist expertise, impact on workflow and efficiency, and performance monitoring.

Ultrasound (US) is the first imaging modality for thyroid parenchyma evaluation. In the last decades, the role of ultrasound has been improved with the introduction of new US software, such as contrast-enhanced ultrasound (CEUS) and US-elastography (USE). USE is nowadays recognized as an essential part of the multiparametric ultrasound (MPUS) examination, in particular for the indeterminate thyroid nodule with possible fine-needle aspiration cytology (FNAC) number reduction; even if further and larger studies are needed to validate it. More controversial is the role of CEUS in thyroid evaluation, due to its high variability in sensitivity and specificity. Semi-automatic US systems based on the computer-aided diagnosis (CAD) system are producing interesting results, especially as an aid to less experienced operators. New knowledge on the molecular mechanisms involved in thyroid cancer is allowing practitioners to identify new genomic thyroid markers that could reduce the number of “diagnostic” thyroidectomies. We have therefore drawn up an updated representation of the current evidence in the literature for thyroid nodule multiparametric ultrasound (MPUS) evaluation with particular regard to USE, the US CAD system and CEUS.

Ultrasound (US) is the primary tool for evaluating patients with thyroid nodules, and the risk of malignancy assessed is based on US features. These features help determine which patients require fine-needle aspiration (FNA) biopsy. Classification systems for US features have been developed to facilitate efficient interpretation, reporting, and communication of thyroid US findings. These systems have been validated by numerous studies and are reviewed in this article. Additionally, this overview provides a comprehensive description of the clinical and laboratory evaluation of patients with thyroid nodules, various imaging modalities, grayscale US features, color Doppler US, contrast-enhanced US (CEUS), US elastography, FNA biopsy assessment, and the recent introduction of molecular testing. The potential of artificial intelligence in thyroid US is also discussed.

Renal transplantation (RT) is the treatment of choice for end-stage renal disease, significantly improving patients’ survival and quality of life. However, approximately 3–23% of patients encounter post-operative complications, and radiology plays a major role for their early detection and treatment or follow-up planning. CT and MRI are excellent imaging modalities to evaluate renal transplant post-operative course; nevertheless, they are both associated with a high cost and low accessibility, as well as some contraindications, making them not feasible for all patients. In particular, gadolinium-based contrast can lead to the rare condition of nephrogenic systemic fibrosis, and iodine-based contrast can lead to contrast-induced nephropathy (CIN). CT also exposes the patients who may require multiple examinations to ionizing radiation. Therefore, considering the overall advantages and disadvantages, contrast-enhanced ultrasound (CEUS) is presently considered an effective first-line imaging modality for post-operative early and long-term follow-up in RT, reducing the need for biopsies and providing adequate guidance for drainage procedures. Hence, this paper aims to review the updated knowledge on CEUS compared with CT and MRI for the evaluation of RT renal transplant complications; advantages, limitations, and possible recommendations are provided.

Atherosclerotic plaque in the carotid artery is the main cause of ischemic stroke, with a high incidence rate among people over 65 years. A timely and precise diagnosis can help to prevent the ischemic event and decide patient management, such as follow up, medical, or surgical treatment. Presently, diagnostic imaging techniques available include color-Doppler ultrasound, as a first evaluation technique, computed tomography angiography, which, however, uses ionizing radiation, magnetic resonance angiography, still not in widespread use, and cerebral angiography, which is an invasively procedure reserved for therapeutically purposes. Contrast-enhanced ultrasound is carving out an important and emerging role which can significantly improve the diagnostic accuracy of an ultrasound. Modern ultrasound technologies, still not universally utilized, are opening new horizons in the arterial pathologies research field. In this paper, the technical development of various carotid artery stenosis diagnostic imaging modalities and their impact on clinical efficacy is thoroughly reviewed.