 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 L01.313.500.750.100.158.600.680
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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]
- Informatics [L01.313]
- Medical Informatics [L01.313.500]
- Medical Informatics Applications [L01.313.500.750]
- Decision Making, Computer-Assisted [L01.313.500.750.100]
- Diagnosis, Computer-Assisted [L01.313.500.750.100.158]
- Image Interpretation, Computer-Assisted [L01.313.500.750.100.158.600]
- Radiographic Image Interpretation, Computer-Assisted [L01.313.500.750.100.158.600.680]
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 |
|---|
| 1996 | 1 | 0 | 1 | | 2001 | 1 | 0 | 1 | | 2003 | 2 | 0 | 2 | | 2004 | 1 | 0 | 1 | | 2006 | 2 | 0 | 2 | | 2007 | 2 | 1 | 3 | | 2008 | 2 | 2 | 4 | | 2009 | 4 | 1 | 5 | | 2010 | 1 | 2 | 3 | | 2011 | 3 | 3 | 6 | | 2012 | 4 | 1 | 5 | | 2013 | 1 | 5 | 6 | | 2014 | 5 | 4 | 9 | | 2015 | 5 | 3 | 8 | | 2016 | 1 | 4 | 5 | | 2017 | 2 | 1 | 3 | | 2018 | 3 | 1 | 4 | | 2019 | 3 | 1 | 4 | | 2020 | 1 | 1 | 2 | | 2021 | 1 | 0 | 1 | | 2023 | 0 | 1 | 1 |
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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Konz N, Buda M, Gu H, Saha A, Yang J, Chledowski J, Park J, Witowski J, Geras KJ, Shoshan Y, Gilboa-Solomon F, Khapun D, Ratner V, Barkan E, Ozery-Flato M, Mart? R, Omigbodun A, Marasinou C, Nakhaei N, Hsu W, Sahu P, Hossain MB, Lee J, Santos C, Przelaskowski A, Kalpathy-Cramer J, Bearce B, Cha K, Farahani K, Petrick N, Hadjiiski L, Drukker K, Armato SG, Mazurowski MA. A Competition, Benchmark, Code, and Data for Using Artificial Intelligence to Detect Lesions in Digital Breast Tomosynthesis. JAMA Netw Open. 2023 02 01; 6(2):e230524.
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Eng DK, Khandwala NB, Long J, Fefferman NR, Lala SV, Strubel NA, Milla SS, Filice RW, Sharp SE, Towbin AJ, Francavilla ML, Kaplan SL, Ecklund K, Prabhu SP, Dillon BJ, Everist BM, Anton CG, Bittman ME, Dennis R, Larson DB, Seekins JM, Silva CT, Zandieh AR, Langlotz CP, Lungren MP, Halabi SS. Artificial Intelligence Algorithm Improves Radiologist Performance in Skeletal Age Assessment: A Prospective Multicenter Randomized Controlled Trial. Radiology. 2021 12; 301(3):692-699.
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Schwartz FR, Khawaja RDA, Marin D, Patel BN, Gray AL, Reynolds JM, Koweek LH. Split-Bolus, Single-Acquisition, Dual-Phase Abdominopelvic CT Angiography for the Evaluation of Lung Transplant Candidates: Image Quality and Resource Utilization. AJR Am J Roentgenol. 2020 12; 215(6):1520-1527.
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Groves DW, Acharya T, Steveson C, Schuzer JL, Rollison SF, Nelson EA, Sirajuddin A, Sathya B, Bronson K, Shanbhag SM, Chen MY. Performance of single-energy metal artifact reduction in cardiac computed tomography: A clinical and phantom study. J Cardiovasc Comput Tomogr. 2020 Nov - Dec; 14(6):510-515.
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Warsavage T, Xing F, Bar?n AE, Feser WJ, Hirsch E, Miller YE, Malkoski S, Wolf HJ, Wilson DO, Ghosh D. Quantifying the incremental value of deep learning: Application to lung nodule detection. PLoS One. 2020; 15(4):e0231468.
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Huo D, Kiehn M, Scherzinger A. Investigation of Low-Dose CT Lung Cancer Screening Scan "Over-Range" Issue Using Machine Learning Methods. J Digit Imaging. 2019 12; 32(6):931-938.
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Tao S, Rajendran K, Zhou W, Fletcher JG, McCollough CH, Leng S. Improving iodine contrast to noise ratio using virtual monoenergetic imaging and prior-knowledge-aware iterative denoising (mono-PKAID). Phys Med Biol. 2019 05 16; 64(10):105014.
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Koirala N, Chauhan N, Thompson D, Karimloo Z, Wunderle K, McLennan G. Quantification of Blood Flow in Dialysis Access Using Custom-Acquisition Protocol and Imaging Methods: A Clinical Validation Study. J Vasc Interv Radiol. 2019 Jul; 30(7):1062-1068.e2.
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Castillo E, Castillo R, Vinogradskiy Y, Dougherty M, Solis D, Myziuk N, Thompson A, Guerra R, Nair G, Guerrero T. Robust CT ventilation from the integral formulation of the Jacobian. Med Phys. 2019 May; 46(5):2115-2125.
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Weinman JP, Mirsky DM, Jensen AM, Stence NV. Dual energy head CT to maintain image quality while reducing dose in pediatric patients. Clin Imaging. 2019 May - Jun; 55:83-88.
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