 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 | 20 | 4 | 24 | | 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 | 3 | 4 | | 2018 | 0 | 1 | 1 | | 2019 | 2 | 1 | 3 |
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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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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Jones KC, Redler G, Templeton A, Bernard D, Turian JV, Chu JCH. Characterization of Compton-scatter imaging with an analytical simulation method. Phys Med Biol. 2018 01 11; 63(2):025016.
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MacMahon H. Using Computer Analysis to Predict Likelihood of Cancer in Lung Nodules. Radiology. 2018 01; 286(1):296-297.
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Nishikawa RM, Bae KT. Importance of Better Human-Computer Interaction in the Era of Deep Learning: Mammography Computer-Aided Diagnosis as a Use Case. J Am Coll Radiol. 2018 01; 15(1 Pt A):49-52.
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Rydzak CE, Armato SG, Avila RS, Mulshine JL, Yankelevitz DF, Gierada DS. Quality assurance and quantitative imaging biomarkers in low-dose CT lung cancer screening. Br J Radiol. 2018 Oct; 91(1090):20170401.
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Rassouli N, Chalian H, Rajiah P, Dhanantwari A, Landeras L. Assessment of 70-keV virtual monoenergetic spectral images in abdominal CT imaging: A comparison study to conventional polychromatic 120-kVp images. Abdom Radiol (NY). 2017 10; 42(10):2579-2586.
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Wang J, Nishikawa RM, Yang Y. Quantitative comparison of clustered microcalcifications in for-presentation and for-processing mammograms in full-field digital mammography. Med Phys. 2017 Jul; 44(7):3726-3738.
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Reichman TW, Fiorello B, Carmody I, Bohorquez H, Cohen A, Seal J, Bruce D, Loss GE. Using on-site liver 3-D reconstruction and volumetric calculations in split liver transplantation. Hepatobiliary Pancreat Dis Int. 2016 Dec; 15(6):587-592.
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