OBJECTIVE: Our aim was to review the evidence concerning the non-invasive diagnosis of endometriosis in adolescents.
METHODS: A systematic review was written following the SWiM reporting guidelines. The study research was made across three databases (MEDLINE/PubMed, Scopus, and Web of Science) to identify articles about the adolescent population and the diagnosis of endometriosis through non-invasive methods. The search included the keywords \"endometriosis,\" \"adolescents,\" \"diagnosis,\" \"ultrasound,\" and \"MRI.\" Only English-language articles were considered, and those published prior to 2000 were excluded. The established outcomes focused on clinical symptoms, ultrasound (US), and magnetic resonance imaging (MRI) findings suggestive of endometriosis.
RESULTS: We included 26 articles, mostly comprising case series and cross-sectional studies. The pooled analysis involved 2,299 female adolescents (age range 8-25 years old) with clinically suspected, imaged, and/or surgically confirmed endometriosis. The most frequently reported symptom was dysmenorrhea, followed by chronic pelvic pain. Among adolescents clinically suspected of endometriosis undergoing ultrasound (US), 32.8% exhibited at least one sign of endometriosis. Of the 167 patients with ultrasound-diagnosed endometriosis, 48.5% had deep infiltrating endometriosis (DIE), and 45.5% had an endometrioma detected. Three studies assessed MRI findings, revealing that 49.8% presented with signs of endometriosis.
CONCLUSIONS: Dysmenorrhea and chronic pelvic pain stand out as key symptoms of adolescent endometriosis. Although their diagnostic accuracy varies, US and MRI have emerged as valuable tools for diagnosing the disease. While the US may have limitations, especially in detecting subtle lesions, MRI shows promise, even in cases with normal previous ultrasounds. Early recognition and proactive diagnosis are crucial for improving the management of endometriosis in adolescents.

Parathyroid pathologies are suspected based on the biochemical alterations and clinical manifestations, and the predominant roles of imaging in primary hyperparathyroidism are localisation of tumour within parathyroid glands, surgical planning, and to look for any ectopic parathyroid tissue in the setting of recurrent disease. This article provides a comprehensive review of embryology and anatomical variations of parathyroid glands and their clinical relevance, surgical anatomy of parathyroid glands, differentiation between multiglandular parathyroid disease, solitary adenoma, atypical parathyroid tumour, and parathyroid carcinoma. The roles, advantages and limitations of ultrasound, four-dimensional computed tomography (4DCT), radiolabelled technetium-99 (99mTc) sestamibi or dual tracer 99mTc pertechnetate and 99mTc-sestamibi with or without single photon emission computed tomography (SPECT) or SPECT/CT, dynamic enhanced magnetic resonance imaging (4DMRI), and fluoro-choline positron emission tomography (18F-FCH PET) or [11C] Methionine (11C -MET) PET in the management of parathyroid lesions have been extensively discussed in this article. The role of fluorodeoxyglucose PET (FDG-PET) has also been elucidated in this article. Management guidelines for parathyroid carcinoma proposed by the American Society of Clinical Oncology (ASCO) have also been described. An algorithm for management of parathyroid lesions has been provided at the end to serve as a quick reference guide for radiologists, clinicians and surgeons.

BACKGROUND: Vocal fold paralysis (VFP), involving one or both vocal folds, often indicates underlying pathologies. Identifying VFP causes is vital for excluding malignancies and focusing on treating the cause. While various imaging methods are used to investigate VFP causes, their detection abilities remain unclear. This study aims to assess the detection prevalence of different imaging techniques in determining the causes of VFP.
METHODS: In September 2023 a comprehensive search was conducted per the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) guidelines across multiple databases, including Web of Science, PubMed, Scopus, Cochrane CENTRAL, and EMBASE. Following the search, the retrieved studies were screened based on the predefined eligibility criteria. Data extraction from the included studies was carried out independently by two authors. Outcomes were analyzed using pooled proportions and 95% confidence intervals.
RESULTS: Our meta-analysis encompassed 14 studies with 1492 VFP patients included. Malignant causes for VFP identification were most prevalent in F-fluorodeoxyglucose Positron Emission Tomography (PET)/Computed Tomography (CT) (41.5%) followed by Magnetic resonance imaging (MRI) (40%), with CT being the lowest (17.1%). Conversely, benign causes had the highest prevalence in F-fluorodeoxyglucose PET/CT (10.8%), followed by MRI (6.7%) and CT (4%). In the VFP cause identification, MRI had the highest detection prevalence (58.1%), followed by CT (30.1%), and Ultra Sound (US) had the lowest (26.8%). In chest lesion detection, CT had the highest prevalence (17.6%), followed by Chest X-ray (CXR) (6.5%). Head lesions were detected with CT at a prevalence of 15%, while neck lesion detection showed CT prevalence at 38.9% and US at 20.6%.
CONCLUSIONS: Our study revealed varying prevalence rates for the identification of malignant and benign causes across different imaging modalities. MRI demonstrated the highest overall detection prevalence for VFP causes, while CT was most commonly used and had the highest prevalence for specific lesions detection in various regions. These findings provide valuable insights into the diagnostic utility of different imaging techniques in the evaluation of VFP.

BACKGROUND: Artificial intelligence (AI), more specifically large language models (LLMs), holds significant potential in revolutionizing emergency care delivery by optimizing clinical workflows and enhancing the quality of decision-making. Although enthusiasm for integrating LLMs into emergency medicine (EM) is growing, the existing literature is characterized by a disparate collection of individual studies, conceptual analyses, and preliminary implementations. Given these complexities and gaps in understanding, a cohesive framework is needed to comprehend the existing body of knowledge on the application of LLMs in EM.
OBJECTIVE: Given the absence of a comprehensive framework for exploring the roles of LLMs in EM, this scoping review aims to systematically map the existing literature on LLMs\' potential applications within EM and identify directions for future research. Addressing this gap will allow for informed advancements in the field.
METHODS: Using PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) criteria, we searched Ovid MEDLINE, Embase, Web of Science, and Google Scholar for papers published between January 2018 and August 2023 that discussed LLMs\' use in EM. We excluded other forms of AI. A total of 1994 unique titles and abstracts were screened, and each full-text paper was independently reviewed by 2 authors. Data were abstracted independently, and 5 authors performed a collaborative quantitative and qualitative synthesis of the data.
RESULTS: A total of 43 papers were included. Studies were predominantly from 2022 to 2023 and conducted in the United States and China. We uncovered four major themes: (1) clinical decision-making and support was highlighted as a pivotal area, with LLMs playing a substantial role in enhancing patient care, notably through their application in real-time triage, allowing early recognition of patient urgency; (2) efficiency, workflow, and information management demonstrated the capacity of LLMs to significantly boost operational efficiency, particularly through the automation of patient record synthesis, which could reduce administrative burden and enhance patient-centric care; (3) risks, ethics, and transparency were identified as areas of concern, especially regarding the reliability of LLMs\' outputs, and specific studies highlighted the challenges of ensuring unbiased decision-making amidst potentially flawed training data sets, stressing the importance of thorough validation and ethical oversight; and (4) education and communication possibilities included LLMs\' capacity to enrich medical training, such as through using simulated patient interactions that enhance communication skills.
CONCLUSIONS: LLMs have the potential to fundamentally transform EM, enhancing clinical decision-making, optimizing workflows, and improving patient outcomes. This review sets the stage for future advancements by identifying key research areas: prospective validation of LLM applications, establishing standards for responsible use, understanding provider and patient perceptions, and improving physicians\' AI literacy. Effective integration of LLMs into EM will require collaborative efforts and thorough evaluation to ensure these technologies can be safely and effectively applied.

Breast pain is extremely common, occurring in 70% to 80% of women. Most cases of breast pain are from physiologic or benign causes, and patients should be reassured and offered treatment strategies to alleviate symptoms, often without diagnostic imaging. A complete clinical history and physical examination is key for distinguishing intrinsic breast pain from extramammary pain. Breast pain without other suspicious symptoms and with a negative history and physical examination result is rarely associated with malignancy, although it is a common reason for women to undergo diagnostic imaging. When breast imaging is indicated, guidelines according to the American College of Radiology Appropriateness Criteria should be followed as to whether mammography, US, or both are recommended. This review article summarizes the initial clinical evaluation of breast pain and evidence-based guidelines for imaging. Additionally, the article reviews cyclical and noncyclical breast pain and provides an image-rich discussion of the imaging presentation and management of benign and malignant breast pain etiologies.

In the United States, silicone and saline breast implants with their familiar radiologic appearance are the mainstays of breast augmentation. However, less well-known sequelae of unconventional injected materials introduced for cosmetic and noncosmetic purposes may also be encountered on breast imaging-for example, free silicone, paraffin and/or oil, polyacrylamide gel, autologous fat, and hyaluronic acid, which are encountered in the setting of breast augmentation. Breast injection of go-yak is not cosmetic but also results in characteristic imaging findings. Breast changes due to extravasation of chemotherapy or interstitial brachytherapy can mimic the appearance of injected noncosmetic materials. Because many of these materials can mimic or obscure imaging findings of breast cancer, it is important to recognize their varied appearances and the limitations of imaging alone in delineating breast injection material from cancer. Given the relatively uncommon incidence of injected materials into the breast, this article aims to review the imaging appearance in order to aid radiologists in maximizing cancer detection and ensuring optimal patient management.

Artificial intelligence (AI) has been a topic of substantial interest for radiologists in recent years. Although many of the first clinical applications were in the neuro, cardiothoracic, and breast imaging subspecialties, the number of investigated and real-world applications of body imaging has been increasing, with more than 30 FDA-approved algorithms now available for applications in the abdomen and pelvis. In this manuscript, we explore some of the fundamentals of artificial intelligence and machine learning, review major functions that AI algorithms may perform, introduce current and potential future applications of AI in abdominal imaging, provide a basic understanding of the pathways by which AI algorithms can receive FDA approval, and explore some of the challenges with the implementation of AI in clinical practice.

The carotid artery is a major blood vessel that supplies blood to the brain. Plaque buildup in the arteries can lead to cardiovascular diseases such as atherosclerosis, stroke, ruptured arteries, and even death. Both invasive and non-invasive methods are used to detect plaque buildup in the arteries, with ultrasound imaging being the first line of diagnosis. This paper presents a comprehensive review of the existing literature on ultrasound image analysis methods for detecting and characterizing plaque buildup in the carotid artery. The review includes an in-depth analysis of datasets; image segmentation techniques for the carotid artery plaque area, lumen area, and intima-media thickness (IMT); and plaque measurement, characterization, classification, and stenosis grading using deep learning and machine learning. Additionally, the paper provides an overview of the performance of these methods, including challenges in analysis, and future directions for research.

Ultrasound (US) is a non-invasive imaging technique frequently used to examine internal organs and superficial tissues, and invaluable in pediatric patients. In a surgical setting, intraoperative ultrasound allows to highlight anatomical structures in detail during traditional open and minimally invasive surgery, thanks to the use of specific probes. In fact, laparoscopic and robotic ultrasonography requires the development of specialized transducers that fit through laparoscopic trocars. In adults, laparoscopic ultrasound is used during cholecystectomy before dissection of the triangle of Calot, to guide liver biopsies and ablation procedures and for the staging of patients with pancreas adenocarcinoma. However, the applications in the pediatric field are still limited. This paper aims to share our preliminary experience with ultra-sound in minimally invasive laparoscopic and robotic pediatric surgery, describing two cases in which intra-operative ultrasound was applied, and to present a review of the literature on the state of the art of the actual uses in pediatric surgery.

By the end of 2020, more than 500,000 migrants from Central America, Haiti, Africa, and Asia sought asylum along the US-Mexico border despite COVID-19-related travel restrictions and public health measures. A scoping review was conducted to understand the role of COVID-19-related policies on irregular migration flows through Central America and Mexico and to examine the experiences of asylum seekers traversing this region. Peer-reviewed literature, policy briefs, and commentaries were screened for inclusion, resulting in 33 documents selected for this review. This review identified three dominant themes: border closures due to multiple national migration policies, delays in asylum procedures, and increased risks to migrant wellbeing. This article argues that border closures were a punitive policy measure to deter irregular migration during the COVID-19 pandemic. Implications for future research and policy include prioritizing the health needs of asylum seekers and advocating the appropriateness and effectiveness of immigration and public health policy.