ABSTRACT
The response to pandemic-related teaching disruption has revealed dynamic levels of learning and teaching flexibility and rapid technology adoption of radiology educators and trainees. Shutdowns and distancing requirements accelerated the adoption of technology as an educational tool, in some instances supplanting in-person education entirely. Despite the limitations of remote interaction, many educational advantages were recognized that can be leveraged in developing distance learning paradigms. The specific strategies employed should match modern learning science, enabling both students and educators to mutually grow as lifelong learners. As panel members of the "COVID: Faculty perspective" Task Force of the Association of University Radiologists Radiology Research Alliance, we present a review of key learning principles which educators can use to identify techniques that enhance resident learning and present an organized framework for applying technology-aided techniques aligned with modern learning principles. Our aim is to facilitate the purposeful integration of learning tools into the training environment by matching these tools to established educational frameworks. With these frameworks in mind, radiology educators have the opportunity to re-think the balance between traditional curricular design and modern digital teaching tools and models.
ABSTRACT
BACKGROUND: The coronavirus disease 2019 (COVID-19) pandemic has disrupted the world since 2019, causing significant morbidity and mortality in developed and developing countries alike. Although substantial resources have been diverted to developing diagnostic, preventative, and treatment measures, disparities in the availability and efficacy of these tools vary across countries. We seek to assess the ability of commercial artificial intelligence (AI) technology to diagnose COVID-19 by analyzing chest radiographs. MATERIALS AND METHODS: Chest radiographs taken from symptomatic patients within two days of polymerase chain reaction (PCR) tests were assessed for COVID-19 infection by board-certified radiologists and commercially available AI software. Sixty patients with negative and 60 with positive COVID reverse transcription-polymerase chain reaction (RT-PCR) tests were chosen. Results were compared against results of the PCR test for accuracy and statistically analyzed by receiver operating characteristic (ROC) curves along with area under the curve (AUC) values. RESULTS: A total of 120 chest radiographs (60 positive and 60 negative RT-PCR tests) radiographs were analyzed. The AI software performed significantly better than chance (p = 0.001) and did not differ significantly from the radiologist ROC curve (p = 0.78). CONCLUSION: Commercially available AI software was not inferior compared with trained radiologists in accurately identifying COVID-19 cases by analyzing radiographs. While RT-PCR testing remains the standard, current advances in AI help correctly analyze chest radiographs to diagnose COVID-19 infection.
ABSTRACT
In this review, we summarize early pulmonary complications related to cancer therapy in children and highlight characteristic findings on imaging that should be familiar to a radiologist reviewing imaging from pediatric cancer patients.