Top Papers: Radiologie (2020)

Die 50 meistzitierten Arbeiten zu Radiologie aus dem Jahr 2020 (von 3.041 insgesamt).

Die Radiologie gehört zu den Fachbereichen, in denen digitale Technologien besonders großen Einfluss haben. Automatisierte Befundungssysteme und KI-gestützte Bildanalysen ergänzen zunehmend die Arbeit von Radiologen. Gleichzeitig verändern sich Ausbildungskonzepte und Qualitätsstandards. Diese Übersicht zeigt die einflussreichsten Arbeiten und aktuellen Trends in der radiologischen Forschung.

2018 2019 2020 2021

#	Paper	Zitationen
1	Medical Student Education in the Time of COVID-19 Suzanne Rose JAMA	1.773
2	Artificial intelligence versus clinicians: systematic review of design, reporting standards, and claims of deep learning studies Myura Nagendran, Yang Chen, Christopher A. Lovejoy et al. BMJ	1.013
3	Proposal for International Standardization of the Use of Lung Ultrasound for Patients With <scp>COVID</scp>‐19 Gino Soldati, Andrea Smargiassi, Riccardo Inchingolo et al. Journal of Ultrasound in Medicine	615
4	Attitudes and perceptions of UK medical students towards artificial intelligence and radiology: a multicentre survey Cherry Sit, Rohit Srinivasan, Ashik Amlani et al. Insights into Imaging	479
5	The Reliability, Validity, and Utility of Self-Assessment John A. Ross —	455
6	Medical education during pandemics: a UK perspective Areeb Mian, Shujhat Khan BMC Medicine	389
7	Point‐of‐care lung ultrasound in patients with <scp>COVID</scp> ‐19 – a narrative review Mike Smith, Simon Hayward, Sue Innes et al. Anaesthesia	323
8	Radiology Department Preparedness for COVID-19: <i>Radiology</i> Scientific Expert Review Panel Mahmud Mossa‐Basha, Carolyn C. Meltzer, Daniel Kim et al. Radiology	318
9	Implementation of artificial intelligence (AI) applications in radiology: hindering and facilitating factors L.G.D. Strohm, Charisma Hehakaya, Erik Ranschaert et al. European Radiology	298
10	What’s new in lung ultrasound during the COVID-19 pandemic Giovanni Volpicelli, Alessandro Lamorte, Tomás Villén Intensive Care Medicine	280
11	Handheld Point-of-Care Ultrasound Probes: The New Generation of POCUS Yanick Baribeau, Aidan Sharkey, Omar Chaudhary et al. Journal of Cardiothoracic and Vascular Anesthesia	262
12	Staff radiation dose and estimated risk in an interventional radiology department M. Alkhorayef, Huda I. Almohammed, Fareed H. Mayhoub et al. Radiation Physics and Chemistry	260
13	COVID-19 classification by CCSHNet with deep fusion using transfer learning and discriminant correlation analysis Shuihua Wang‎, Deepak Ranjan Nayak, David S. Guttery et al. Information Fusion	240
14	Patient Exposure from Radiologic and Nuclear Medicine Procedures in the United States: Procedure Volume and Effective Dose for the Period 2006–2016 Fred A. Mettler, Mahadevappa Mahesh, Mythreyi Bhargavan et al. Radiology	225
15	Continuous Learning AI in Radiology: Implementation Principles and Early Applications Oleg S. Pianykh, Georg Langs, Marc Dewey et al. Radiology	223
16	Contrastive Cross-Site Learning With Redesigned Net for COVID-19 CT Classification Zhao Wang, Quande Liu, Qi Dou IEEE Journal of Biomedical and Health Informatics	219
17	Recommendations for Echocardiography Laboratories Participating in Cardiac Point of Care Cardiac Ultrasound (POCUS) and Critical Care Echocardiography Training: Report from the American Society of Echocardiography James N. Kirkpatrick, Richard A. Grimm, Amer M. Johri et al. Journal of the American Society of Echocardiography	212
18	The Economic Impact of the COVID-19 Pandemic on Radiology Practices Joseph J. Cavallo, Howard P. Forman Radiology	211
19	Artificial Intelligence in Low- and Middle-Income Countries: Innovating Global Health Radiology Daniel J. Mollura, Melissa P. Culp, Erica Pollack et al. Radiology	207
20	Our Italian experience using lung ultrasound for identification, grading and serial follow‐up of severity of lung involvement for management of patients with COVID‐19 Luigi Vetrugno, Tiziana Bove, Daniele Orso et al. Echocardiography	202
21	Improving Image Quality and Reducing Radiation Dose for Pediatric CT by Using Deep Learning Reconstruction Samuel L. Brady, Andrew T. Trout, Elanchezhian Somasundaram et al. Radiology	191
22	DeepCOVID-XR: An Artificial Intelligence Algorithm to Detect COVID-19 on Chest Radiographs Trained and Tested on a Large U.S. Clinical Data Set Ramsey M. Wehbe, Jiayue Sheng, Shinjan Dutta et al. Radiology	186
23	The Impact of COVID-19 on Radiology Trainees Matthew D. Alvin, Elizabeth George, Francis Deng et al. Radiology	182
24	RETRACTED ARTICLE: COVID-CheXNet: hybrid deep learning framework for identifying COVID-19 virus in chest X-rays images Alaa S. Al‐Waisy, Shumoos Al-Fahdawi, Mazin Abed Mohammed et al. Soft Computing	179
25	Coronavirus disease 2019 (COVID-19) imaging reporting and data system (COVID-RADS) and common lexicon: a proposal based on the imaging data of 37 studies Sana Salehi, Aidin Abedi, S. Balakrishnan et al. European Radiology	178
26	PENet—a scalable deep-learning model for automated diagnosis of pulmonary embolism using volumetric CT imaging Shih-Cheng Huang, Tanay Kothari, Imon Banerjee et al. npj Digital Medicine	177
27	Deep learning workflow in radiology: a primer Emmanuel Montagnon, Milena Cerny, Alexandre Cadrin-Chênevert et al. Insights into Imaging	176
28	Policies and Guidelines for COVID-19 Preparedness: Experiences from the University of Washington Mahmud Mossa‐Basha, Jonathan R. Medverd, Ken F. Linnau et al. Radiology	176
29	Workload for radiologists during on-call hours: dramatic increase in the past 15 years Rik J. M. Bruls, Robert M. Kwee Insights into Imaging	170
30	Journal of the American College of Radiology Daniel Bell, Arlene Campos Radiopaedia.org	170
31	Artificial intelligence: radiologists’ expectations and opinions gleaned from a nationwide online survey Francesca Coppola, Lorenzo Faggioni, Daniele Regge et al. La radiologia medica	167
32	Medical Student Perspectives on the Impact of Artificial Intelligence on the Practice of Medicine Christian Park, Paul H. Yi, Eliot L. Siegel Current Problems in Diagnostic Radiology	161
33	Development of a Deep Learning Algorithm for Periapical Disease Detection in Dental Radiographs Michael G. Endres, Florian Hillen, Marios Salloumis et al. Diagnostics	161
34	The Battle Against Coronavirus Disease 2019 (COVID-19): Emergency Management and Infection Control in a Radiology Department Zixing Huang, Shuang Zhao, Zhenlin Li et al. Journal of the American College of Radiology	152
35	Deep transfer learning artificial intelligence accurately stages COVID-19 lung disease severity on portable chest radiographs Jocelyn Zhu, Beiyi Shen, Almas Abbasi et al. PLoS ONE	149
36	Comparison of Chest Radiograph Interpretations by Artificial Intelligence Algorithm vs Radiology Residents Joy T. Wu, Ken C. L. Wong, Yaniv Gur et al. JAMA Network Open	147
37	COVID-19 Deep Learning Prediction Model Using Publicly Available Radiologist-Adjudicated Chest X-Ray Images as Training Data: Preliminary Findings Mohd Zulfaezal Che Azemin, Radhiana Hassan, Mohd Izzuddin Mohd Tamrin et al. International Journal of Biomedical Imaging	147
38	Chest ultrasonography versus supine chest radiography for diagnosis of pneumothorax in trauma patients in the emergency department Kenneth K. Chan, Daniel Joo, Andrew D. McRae et al. Cochrane Database of Systematic Reviews	144
39	Artificial Intelligence System Approaching Neuroradiologist-level Differential Diagnosis Accuracy at Brain MRI Andreas M. Rauschecker, Jeffrey D. Rudie, Long Xie et al. Radiology	144
40	Correlation between Chest Computed Tomography and Lung Ultrasonography in Patients with Coronavirus Disease 2019 (COVID-19) Yale Tung‐Chen, M. Martí De Gracia, Á. Díez Tascón et al. Ultrasound in Medicine & Biology	143
41	Barriers to learning and using point-of-care ultrasound: a survey of practicing internists in six North American institutions Jonathan Wong, Steven J. Montague, Paul Wallace et al. The Ultrasound Journal	143
42	Automated Lung Ultrasound B-Line Assessment Using a Deep Learning Algorithm Cristiana Baloescu, Grzegorz Toporek, Seungsoo Kim et al. IEEE Transactions on Ultrasonics Ferroelectrics and Frequency Control	142
43	Why, when, and how to use lung ultrasound during the COVID-19 pandemic: enthusiasm and caution Luna Gargani, Hatem Soliman-Aboumarie, Giovanni Volpicelli et al. European Heart Journal - Cardiovascular Imaging	140
44	Technology Enhanced Assessment (TEA) in COVID 19 Pandemic Rehan Ahmed Khan, Masood Jawaid Pakistan Journal of Medical Sciences	140
45	Artificial Intelligence in Radiology—Ethical Considerations Adrian P. Brady, Emanuele Neri Diagnostics	140
46	Guidelines for Online Assessment in Emergency Remote Teaching during the COVID-19 Pandemic Ahmad Fuad Abdul Rahim Education in Medicine Journal	137
47	Consensus statement on the content of clinical reasoning curricula in undergraduate medical education Nicola Cooper, Maggie Bartlett, Simon Gay et al. Medical Teacher	136
48	Lung ultrasound findings in patients with COVID-19 pneumonia Changyang Xing, Qiaoying Li, Hong Du et al. Critical Care	133
49	Video Interviewing: A Review and Recommendations for Implementation in the Era of COVID-19 and Beyond Aparna Joshi, David A. Bloom, Amy Spencer et al. Academic Radiology	132
50	Medical Education’s Wicked Problem: Achieving Equity in Assessment for Medical Learners Catherine R. Lucey, Karen E. Hauer, Dowin Boatright et al. Academic Medicine	129

Top Papers: Radiologie (2020)

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