Canadian Association of Radiologists journal = Journal l'Association canadienne des radiologistes
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The Coronavirus disease 2019 (COVID-19) pandemic has altered how medical education is delivered, worldwide. Didactic sessions have transitioned to electronic/online platforms and clinical teaching opportunities are limited. These changes will affect how radiology is taught to medical students at both the pre-clerkship (ie, year 1 and 2) and clinical (ie, year 3 and 4) levels. ⋯ In the clinical learning environment, medical students primarily shadow radiologists and radiology residents and attend radiology resident teaching sessions. These formats of radiology education, which have been the tenets of the specialty, pose significant challenges during the pandemic. This article reviews how undergraduate radiology education is affected by COVID-19 and explores solutions for teaching and learning based on e-learning and blended learning theory.
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Modern advances in the medical imaging layered onto sophisticated trauma resuscitation strategies in highly organized regionalized trauma systems have created a paradigm shift in the management of severely injured patients. Although immediate exploratory surgery to identify and control life-threatening injuries still has its place, accelerated image acquisition and interpretation procedures now make it rare for trauma surgeons in major centers to venture into damage control surgery unaided by computed tomography (CT) or other imaging, particularly in cases of blunt trauma. Indeed, because of the high incidence of clinically occult injuries associated with major mechanism trauma, and even lower energy trauma in frail or elderly patients, CT imaging has become as invaluable as physical examination, if not more so, in critical decision-making in support of optimal outcomes. ⋯ Through standardized guidelines, streamlined protocols, synoptic reporting, accessible web-based platforms, and active collaboration with clinicians, radiologists dedicated to trauma and emergency imaging enable clearer understanding of complex injuries in high-risk patients which leads to superior clinical decision-making. Whereas dated dogma has long warned that the CT scanner is the last place to take a challenging trauma patient, modern practice suggests that, more often than not, early comprehensive imaging can be done safely and efficiently and is in the patient's best interest. This article outlines how the role of diagnostic imaging for major trauma has evolved considerably in recent years.
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Review
Applications of Artificial Intelligence in Musculoskeletal Imaging: From the Request to the Report.
Artificial intelligence (AI) will transform every step in the imaging value chain, including interpretive and noninterpretive components. Radiologists should familiarize themselves with AI developments to become leaders in their clinical implementation. ⋯ Noninterpretive tasks which may be assisted by AI include the ordering of appropriate imaging tests, automatic exam protocoling, optimized scheduling, shorter magnetic resonance imaging acquisition time, computed tomography imaging with reduced artifact and radiation dose, and new methods of generation and utilization of radiology reports. Applications of AI for image interpretation consist of the determination of bone age, body composition measurements, screening for osteoporosis, identification of fractures, evaluation of segmental spine pathology, detection and temporal monitoring of osseous metastases, diagnosis of primary bone and soft tissue tumors, and grading of osteoarthritis.
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There have been many recently published studies exploring machine learning (ML) and deep learning applications within neuroradiology. The improvement in performance of these techniques has resulted in an ever-increasing number of commercially available tools for the neuroradiologist. In this narrative review, recent publications exploring ML in neuroradiology are assessed with a focus on several key clinical domains. In particular, major advances are reviewed in the context of: (1) intracranial hemorrhage detection, (2) stroke imaging, (3) intracranial aneurysm screening, (4) multiple sclerosis imaging, (5) neuro-oncology, (6) head and tumor imaging, and (7) spine imaging.
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Lung cancer remains the most common cause of cancer death worldwide. Recent advances in lung cancer screening, radiotherapy, surgical techniques, and systemic therapy have led to increasing complexity in diagnosis, treatment decision-making, and assessment of recurrence. Artificial intelligence (AI)-based prediction models are being developed to address these issues and may have a future role in screening, diagnosis, treatment selection, and decision-making around salvage therapy. ⋯ However, although exploratory studies demonstrate potential utility, there is a need for rigorous validation and standardization before AI can be utilized in clinical decision-making. In this review, we will provide a summary of the current literature implementing AI for outcome prediction in lung cancer. We will describe the anticipated impact of AI on the management of patients with lung cancer and discuss the challenges of clinical implementation of these techniques.