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
|
| 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
|
| Concept/Terms |
|
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 |
|---|
| 1999 | 1 | 0 | 1 |
| 2001 | 1 | 0 | 1 |
| 2002 | 0 | 1 | 1 |
| 2003 | 0 | 1 | 1 |
| 2004 | 1 | 2 | 3 |
| 2005 | 3 | 2 | 5 |
| 2006 | 3 | 1 | 4 |
| 2007 | 5 | 3 | 8 |
| 2008 | 2 | 3 | 5 |
| 2009 | 9 | 4 | 13 |
| 2010 | 2 | 7 | 9 |
| 2011 | 5 | 4 | 9 |
| 2012 | 13 | 6 | 19 |
| 2013 | 7 | 5 | 12 |
| 2014 | 10 | 10 | 20 |
| 2015 | 8 | 7 | 15 |
| 2016 | 6 | 8 | 14 |
| 2017 | 4 | 13 | 17 |
| 2018 | 5 | 5 | 10 |
| 2019 | 8 | 4 | 12 |
| 2020 | 11 | 3 | 14 |
| 2021 | 0 | 2 | 2 |
| 2022 | 0 | 2 | 2 |
| 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.
-
Detection and size quantification of pulmonary nodules in ultralow-dose versus regular-dose CT: a comparative study in COPD patients. Br J Radiol. 2023 Mar 01; 96(1144):20220709.
-
Optimization of contrast material administration for coronary CT angiography using a software-based test-bolus evaluation algorithm. Br J Radiol. 2022 May 01; 95(1133):20201456.
-
Deep Learning Reconstruction Shows Better Lung Nodule Detection for Ultra-Low-Dose Chest CT. Radiology. 2022 04; 303(1):202-212.
-
Performance of a deep learning-based lung nodule detection system as an alternative reader in a Chinese lung cancer screening program. Eur J Radiol. 2022 Jan; 146:110068.
-
Virtual monoenergetic dual-energy CT reconstructions at 80?keV are optimal non-contrast CT technique for early stroke detection. Neuroradiol J. 2022 Jun; 35(3):337-345.
-
Feasibility of bronchial wall quantification in low- and ultralow-dose third-generation dual-source CT: An ex vivo lung study. J Appl Clin Med Phys. 2020 Oct; 21(10):218-226.
-
Tube Voltage, DNA Double-Strand Breaks, and Image Quality in Coronary CT Angiography. Korean J Radiol. 2020 08; 21(8):967-977.
-
Ischemia and outcome prediction by cardiac CT based machine learning. Int J Cardiovasc Imaging. 2020 Dec; 36(12):2429-2439.
-
Deep learning-based pulmonary nodule detection: Effect of slab thickness in maximum intensity projections at the nodule candidate detection stage. Comput Methods Programs Biomed. 2020 Nov; 196:105620.
-
Discrimination of benign from malignant breast lesions in dense breasts with model-based analysis of regions-of-interest using directional diffusion-weighted images. BMC Med Imaging. 2020 06 09; 20(1):61.