Knowing the ABCs: teaching the principles of radiology to medical students in Turkey.

BACKGROUND: Radiology education in Turkey is mainly taught during clinical years of medical school and often lacks main principles. Exposure to the fundamentals of radiology at an early stage of medical education may drastically help students generate a better understanding of radiology and expand their interest in the specialty. With the Principles of Radiology Course that we provided, pre- and post-session tests, and assessment survey at the end of the course, we aimed to evaluate the effectiveness of such an online course among Turkish medical students. METHODS: A total of nine online sessions on imaging modalities principles was developed by radiology professors. Each session was given through Zoom by radiologists from different U.S.-institutions to Turkish medical students from state (n = 33) and private (n = 8) universities. Pretests and post-tests were given to participants via Qualtrics before and after each session, respectively. Paired two-sample t-tests were conducted to detect the variance and p=-.05 was used as the significance level. An evaluation survey was distributed at the end of the course to collect their feedback through SurveyMonkey. RESULTS: A total of 1,438 predominantly Turkish (99.32%) medical students engaged with this course. An average of 506 students completed both pre-test and post-test. There was a statistically significant (p < .001) increase in the scores in post-test (mean[range]:7.58[5.21-8.53]) relative to pre-test (mean[range]:5.10[3.52-8.53]). Four hundred and thirty-nine participants (F/M:63.33%/35.54%) completed the end-of-course survey. A total of 71% and 69.70% of the participants strongly agreed that the course would be useful in their clinical practice and had increased their understanding of radiology. They also reported that their level of confidence in the subjects had increased 68% and reached a weighted average of 3.09/4. The survey revealed that 396 (90.21%) of the participants strongly or somewhat agree that introductory principles and concepts should be presented in earlier years of medical education. Compared to in-person education, 358 (81.55%) found the course extremely or very convenient. CONCLUSION: Online lecture series consisting of the principles of the radiological imaging modalities can be offered to Turkish medical students to enhance their grasp of the various imaging modalities and their correct clinical application.

The RSNA International COVID-19 Open Radiology Database (RICORD).

The coronavirus disease 2019 (COVID-19) pandemic is a global health care emergency. Although reverse-transcription polymerase chain reaction testing is the reference standard method to identify patients with COVID-19 infection, chest radiography and CT play a vital role in the detection and management of these patients. Prediction models for COVID-19 imaging are rapidly being developed to support medical decision making. However, inadequate availability of a diverse annotated data set has limited the performance and generalizability of existing models. To address this unmet need, the RSNA and Society of Thoracic Radiology collaborated to develop the RSNA International COVID-19 Open Radiology Database (RICORD). This database is the first multi-institutional, multinational, expert-annotated COVID-19 imaging data set. It is made freely available to the machine learning community as a research and educational resource for COVID-19 chest imaging. Pixel-level volumetric segmentation with clinical annotations was performed by thoracic radiology subspecialists for all COVID-19-positive thoracic CT scans. The labeling schema was coordinated with other international consensus panels and COVID-19 data annotation efforts, the European Society of Medical Imaging Informatics, the American College of Radiology, and the American Association of Physicists in Medicine. Study-level COVID-19 classification labels for chest radiographs were annotated by three radiologists, with majority vote adjudication by board-certified radiologists. RICORD consists of 240 thoracic CT scans and 1000 chest radiographs contributed from four international sites. It is anticipated that RICORD will ideally lead to prediction models that can demonstrate sustained performance across populations and health care systems.

Deep COVID DeteCT: an international experience on COVID-19 lung detection and prognosis using chest CT.

The Coronavirus disease 2019 (COVID-19) presents open questions in how we clinically diagnose and assess disease course. Recently, chest computed tomography (CT) has shown utility for COVID-19 diagnosis. In this study, we developed Deep COVID DeteCT (DCD), a deep learning convolutional neural network (CNN) that uses the entire chest CT volume to automatically predict COVID-19 (COVID+) from non-COVID-19 (COVID-) pneumonia and normal controls. We discuss training strategies and differences in performance across 13 international institutions and 8 countries. The inclusion of non-China sites in training significantly improved classification performance with area under the curve (AUCs) and accuracies above 0.8 on most test sites. Furthermore, using available follow-up scans, we investigate methods to track patient disease course and predict prognosis.

Radiological approach to COVID-19 pneumonia with an emphasis on chest CT.

Coronavirus disease 2019 (COVID-19) has recently become a worldwide outbreak with several millions of people infected and more than 160.000 deaths. A fast and accurate diagnosis in this outbreak is critical to isolate and treat patients. Radiology plays an important role in the diagnosis and management of the patients. Among various imaging modalities, chest CT has received attention with its higher sensitivity and specificity rates. Shortcomings of the real-time reverse transcriptase-polymerase chain reaction test, including inappropriate sample collection and analysis methods, initial false negative results, and limited availability has led to widespread use of chest CT in the diagnostic algorithm. This review summarizes the role of radiology in COVID-19 pneumonia, diagnostic accuracy of imaging, and chest CT findings of the disease.