Seminars in Ultrasound Ct and Mri最新文献

Ultrasound in aesthetics presents multiple applications in several areas, including diagnosing fillers and non-fillers complications, the performance of ultrasound-guided injections in real-time, and identifying the most common types of cosmetic fillers. Furthermore, this application of ultrasound in aesthetics has become a must for managing aesthetic patients because, to date, this modality is the one that provides the higher resolution among imaging techniques, detailed anatomical information, and blood flow detection, besides showing worldwide availability. This paper aims to review the current applications of ultrasound in aesthetics based on publications from scientific literature and the authors' experience.

Dermatologic ultrasound has grown exponentially during the last decades and has passed from the experimental phase to the routine daily practice in multiple countries. The performance of this imaging technique requires color Doppler ultrasound devices working with high-frequency probes, a trained operator on imaging and dermatologic conditions, and the performance of standardized protocols for obtaining the anatomical data properly. In this review, we analyze the ultrasonographic anatomy of the skin, hair, and nails, the technical requirements and considerations, the guidelines, and the recommended protocols, and provide the best tips for practicing this type of examination confidently.

Inflammatory cutaneous diseases can be challenging to diagnose and manage. Nowadays, the anatomical information provided by ultrasound is critical for detecting subclinical alterations and assessing the severity and activity of these conditions. Many of these entities can be clinically observed in dermatology and other specialties, such as rheumatology, plastic surgery, ophthalmology, and otolaryngology, among others. We review the ultrasonographic patterns of the most common inflammatory cutaneous conditions. In several cases, such as hidradenitis suppurativa, acne, and morphea, there are ultrasonographic staging systems of severity or activity that are pivotal in the management of these diseases. The early ultrasonographic diagnosis of these entities implies a proper management of the patients and, therefore, improve their quality of life. Thus, knowledge of the current use of ultrasound in this field seems essential.

Diversity, equity, and inclusion (DEI) are fundamental to a just healthcare system, yet academic radiology continues to grapple with the underrepresentation of women and underrepresented minorities (URMs). This study investigates demographic disparities within academic radiology and proposes strategies to enhance DEI. Through analysis of demographic data and a review of successful DEI initiatives, I identified a severe underrepresentation of URMs and women throughout every stage of the radiology pipeline. Challenges such as implicit bias, financial barriers, and lack of mentorship contribute to this disparity. However, promising initiatives like the Radiology Leadership Institute and the Association of University Radiologists Mentorship Program offer examples of progress in diversifying the field. To achieve true DEI in academic radiology, a multifaceted approach is essential, encompassing early outreach, financial aid, mentorship, inclusive recruitment, and a commitment to fostering a welcoming environment. Continuous evaluation and adaptation of these initiatives will pave the way for a more equitable and inclusive future in radiology.

There are approximately 200 academic radiology departments in the United States. While academic medical centers vary widely depending on their size, complexity, medical school affiliation, research portfolio, and geographic location, they are united by their 3 core missions: patient care, education and training, and scholarship. Despite inherent differences, the current challenges faced by all academic radiology departments have common threads; potential solutions and future adaptations will need to be tailored and individualized—one size will not fit all. In this article, we provide an overview based on our experiences at 4 academic centers across the United States, from relatively small to very large size, and discuss creative and innovative ways to adapt, including community expansion, hybrid models of faculty in-person vs teleradiology (traditional vs non-traditional schedule), work-life integration, recruitment and retention, mentorship, among others.

The field of Radiology is continually changing, requiring corresponding evolution in both medical student and resident training to adequately prepare the next generation of radiologists. With advancements in adult education theory and a deeper understanding of perception in imaging interpretation, expert educators are reshaping the training landscape by introducing innovative teaching methods to align with increased workload demands and emerging technologies. These include the use of peer and interdisciplinary teaching, gamification, case repositories, flipped-classroom models, social media, and drawing and comics. This publication aims to investigate these novel approaches and offer persuasive evidence supporting their incorporation into the updated Radiology curriculum.

Over the past 15 years, the radiology community has made great progress moving from a system of score-based peer review to one of peer learning. Much has been learned along the way. In peer learning, cases in which learning opportunities are identified are reviewed solely for the purpose of fostering learning and improvement. This article defines peer learning and peer review and emphasizes the difference; looks back at the 20-year history of score-based peer review and transition to peer learning; outlines the problems with score-based peer review and the key elements of peer learning; discusses the current state of peer learning; and outlines future challenges and opportunities.

Artificial intelligence’s (AI) emergence in radiology elicits both excitement and uncertainty. AI holds promise for improving radiology with regards to clinical practice, education, and research opportunities. Yet, AI systems are trained on select datasets that can contain bias and inaccuracies. Radiologists must understand these limitations and engage with AI developers at every step of the process – from algorithm initiation and design to development and implementation – to maximize benefit and minimize harm that can be enabled by this technology.