 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 | 0 | 2 | 2 | | 2011 | 3 | 3 | 6 | | 2012 | 4 | 1 | 5 | | 2013 | 1 | 5 | 6 | | 2014 | 5 | 4 | 9 | | 2015 | 4 | 3 | 7 | | 2016 | 1 | 4 | 5 | | 2017 | 1 | 1 | 2 | | 2018 | 4 | 1 | 5 | | 2019 | 3 | 2 | 5 | | 2020 | 1 | 2 | 3 | | 2021 | 1 | 0 | 1 | | 2022 | 0 | 1 | 1 | | 2023 | 0 | 1 | 1 | | 2024 | 0 | 1 | 1 | | 2025 | 1 | 1 | 2 |
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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Prince EW, Mirsky DM, Hankinson TC, G?rg C. Impact of AI Decision Support on Clinical Experts' Radiographic Interpretation of Adamantinomatous Craniopharyngioma. AMIA Annu Symp Proc. 2024; 2024:930-939.
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Sollmann N, Mei K, L?ffler MT, R?hling S, Beer M, Zimmer C, Kirschke JS, No?l PB, Baum T, Carballido-Gamio J. Simulated low-dose multi-detector computed tomography: spatial effects on surrogate parameters of bone strength at the proximal femur. Osteoporos Int. 2025 May; 36(5):917-928.
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Hunter JG, Bera K, Ciancibello L, Bukhari SMA, Sposato J, Gilani KA, Gilkeson R, Gupta A. Real-world impact of high-pitch helical CT on radiation exposure and image quality in infants being evaluated for cardiothoracic pathologies. Pediatr Radiol. 2025 Jan; 55(1):151-158.
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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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Laukamp KR, Dastmalchian S, Tandon YK, Ciancibello L, Pennig L, Lennartz S, Al-Kindi S, Rajagopalan S, Bera K, Hokamp NG, Gilkeson R, Gupta A. Imaging of the Left Atrial Appendage Before Occluder Device Placement: Evaluation of Virtual Monoenergetic Images in a Single-Bolus Dual-Phase Protocol. J Comput Assist Tomogr. 2022 Sep-Oct 01; 46(5):735-741.
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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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Gordon AC, Gates VL, White SB, Harris KR, Procissi D, Zhang Z, Li W, Samaan D, Nicolai JR, Mouli SK, Sato KT, Ryu RK, Omary RA, Salem R, Lewandowski RJ, Larson AC. Correlation and Agreement of Yttrium-90 Positron Emission Tomography/Computed Tomography with Ex?Vivo Radioembolization Microsphere Deposition in the Rabbit VX2 Liver Tumor Model. J Vasc Interv Radiol. 2021 01; 32(1):23-32.e1.
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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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