 Radiographic Image Interpretation, Computer-Assisted
"Radiographic Image Interpretation, Computer-Assisted" is a descriptor in the National Library of Medicine's controlled vocabulary thesaurus,
MeSH (Medical Subject Headings). Descriptors are arranged in a hierarchical structure,
which enables searching at various levels of specificity.
Computer systems or networks designed to provide radiographic interpretive information.
| Descriptor ID |
D011857
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| MeSH Number(s) |
E01.158.600.680 E01.370.350.350.700 E01.370.350.700.705 L01.700.508.100.158.600.680
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| Concept/Terms |
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Below are MeSH descriptors whose meaning is more general than "Radiographic Image Interpretation, Computer-Assisted".
- Analytical, Diagnostic and Therapeutic Techniques and Equipment [E]
- Diagnosis [E01]
- Diagnosis, Computer-Assisted [E01.158]
- Image Interpretation, Computer-Assisted [E01.158.600]
- Radiographic Image Interpretation, Computer-Assisted [E01.158.600.680]
- Diagnostic Techniques and Procedures [E01.370]
- Diagnostic Imaging [E01.370.350]
- Image Interpretation, Computer-Assisted [E01.370.350.350]
- Radiographic Image Interpretation, Computer-Assisted [E01.370.350.350.700]
- Radiography [E01.370.350.700]
- Radiographic Image Interpretation, Computer-Assisted [E01.370.350.700.705]
- Information Science [L]
- Information Science [L01]
- Medical Informatics [L01.700]
- Medical Informatics Applications [L01.700.508]
- Decision Making, Computer-Assisted [L01.700.508.100]
- Diagnosis, Computer-Assisted [L01.700.508.100.158]
- Image Interpretation, Computer-Assisted [L01.700.508.100.158.600]
- Radiographic Image Interpretation, Computer-Assisted [L01.700.508.100.158.600.680]
Below are MeSH descriptors whose meaning is more specific than "Radiographic Image Interpretation, Computer-Assisted".
This graph shows the total number of publications written about "Radiographic Image Interpretation, Computer-Assisted" by people in this website by year, and whether "Radiographic Image Interpretation, Computer-Assisted" was a major or minor topic of these publications.
To see the data from this visualization as text, click here.
| Year | Major Topic | Minor Topic | Total |
|---|
| 1987 | 1 | 1 | 2 | | 1988 | 4 | 0 | 4 | | 1990 | 4 | 1 | 5 | | 1991 | 1 | 0 | 1 | | 1992 | 4 | 0 | 4 | | 1993 | 9 | 1 | 10 | | 1994 | 4 | 1 | 5 | | 1995 | 4 | 1 | 5 | | 1996 | 4 | 2 | 6 | | 1997 | 1 | 3 | 4 | | 1998 | 6 | 0 | 6 | | 1999 | 0 | 1 | 1 | | 2000 | 1 | 3 | 4 | | 2001 | 1 | 2 | 3 | | 2002 | 9 | 0 | 9 | | 2003 | 9 | 1 | 10 | | 2004 | 21 | 4 | 25 | | 2005 | 19 | 4 | 23 | | 2006 | 18 | 7 | 25 | | 2007 | 13 | 2 | 15 | | 2008 | 11 | 2 | 13 | | 2009 | 9 | 7 | 16 | | 2010 | 7 | 1 | 8 | | 2011 | 5 | 8 | 13 | | 2012 | 6 | 2 | 8 | | 2013 | 7 | 3 | 10 | | 2014 | 5 | 4 | 9 | | 2015 | 5 | 3 | 8 | | 2016 | 3 | 0 | 3 | | 2017 | 1 | 4 | 5 | | 2018 | 2 | 1 | 3 | | 2019 | 3 | 1 | 4 | | 2020 | 1 | 3 | 4 |
To return to the timeline, click here.
Below are the most recent publications written about "Radiographic Image Interpretation, Computer-Assisted" by people in Profiles.
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Wehbe RM, Sheng J, Dutta S, Chai S, Dravid A, Barutcu S, Wu Y, Cantrell DR, Xiao N, Allen BD, MacNealy GA, Savas H, Agrawal R, Parekh N, Katsaggelos AK. DeepCOVID-XR: An Artificial Intelligence Algorithm to Detect COVID-19 on Chest Radiographs Trained and Tested on a Large U.S. Clinical Data Set. Radiology. 2021 04; 299(1):E167-E176.
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Dhara SS, Hermsen M, Khabaz K, Abbott E, Babrowski TA, Milner R, Lee CJ, Pocivavsek L. Gaussian Surface Curvature Mapping Indicating High Risk Type B Thoracic Aortic Dissections. Ann Vasc Surg. 2021 Jan; 70:171-180.
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Wang CY, Foy JJ, Siewert TY, Haraf DJ, Ginat DT. Baseline Computed Tomography Radiomic and Genomic Assessment of Head and Neck Squamous Cell Carcinoma. J Comput Assist Tomogr. 2020 Jul/Aug; 44(4):546-552.
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Alavanja A, Parker K, Ginat DT. Magnetic Resonance Imaging Findings of Crystalloid-Containing Parotid Cysts. J Comput Assist Tomogr. 2020 Jul/Aug; 44(4):569-570.
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Schonberger M, Lefere P, Dachman AH. Pearls and Pitfalls of Interpretation in CT Colonography. Can Assoc Radiol J. 2020 May; 71(2):140-148.
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Mozaffary A, Trabzonlu TA, Lombardi P, Seyal AR, Agrawal R, Yaghmai V. Integration of fully automated computer-aided pulmonary nodule detection into CT pulmonary angiography studies in the emergency department: effect on workflow and diagnostic accuracy. Emerg Radiol. 2019 Dec; 26(6):609-614.
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Patel AR, Maffessanti F, Patel MB, Kebed K, Narang A, Singh A, Medvedofsky D, Zaidi SJ, Mediratta A, Goyal N, Kachenoura N, Lang RM, Mor-Avi V. Hemodynamic impact of coronary stenosis using computed tomography: comparison between noninvasive fractional flow reserve and 3D fusion of coronary angiography with stress myocardial perfusion. Int J Cardiovasc Imaging. 2019 Sep; 35(9):1733-1743.
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Robinson K, Li H, Lan L, Schacht D, Giger M. Radiomics robustness assessment and classification evaluation: A two-stage method demonstrated on multivendor FFDM. Med Phys. 2019 May; 46(5):2145-2156.
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Balaney B, Vembar M, Grass M, Singh A, Kawaji K, Landeras L, Chung J, Mor-Avi V, Patel AR. Improved visualization of the coronary arteries using motion correction during vasodilator stress CT myocardial perfusion imaging. Eur J Radiol. 2019 May; 114:1-5.
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Drukker K, Giger ML, Joe BN, Kerlikowske K, Greenwood H, Drukteinis JS, Niell B, Fan B, Malkov S, Avila J, Kazemi L, Shepherd J. Combined Benefit of Quantitative Three-Compartment Breast Image Analysis and Mammography Radiomics in the Classification of Breast Masses in a Clinical Data Set. Radiology. 2019 03; 290(3):621-628.
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