A G Rockall
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Explore the profile of A G Rockall including associated specialties, affiliations and a list of published articles.
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35
Citations
572
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Recent Articles
1.
Shelmerdine S, Hickman S, Jackson N, Cronheim D, Taylor J, Swift A, et al.
Clin Radiol
. 2024 Oct;
79(12):892-902.
PMID: 39366889
Radiology currently stands at the forefront of artificial intelligence (AI) development and deployment over many other medical subspecialities within the scope of both research and clinical practice. Given this current...
2.
Ross J, Hammouche S, Chen Y, Rockall A
Clin Radiol
. 2024 Feb;
79(5):338-345.
PMID: 38360516
The implementation of artificial intelligence (AI) applications in routine practice, following regulatory approval, is currently limited by practical concerns around reliability, accountability, trust, safety, and governance, in addition to factors...
3.
Rockall A, Shelmerdine S, Chen M
Clin Radiol
. 2023 Jan;
78(2):81-82.
PMID: 36639174
No abstract available.
4.
Islam S, Kanavati F, Arain Z, Da Costa O, Crum W, Aboagye E, et al.
Clin Radiol
. 2022 Mar;
77(5):e363-e371.
PMID: 35260232
Aim: To develop a fully automated deep-learning-based approach to measure muscle area for assessing sarcopenia on standard-of-care computed tomography (CT) of the abdomen without any case exclusion criteria, for opportunistic...
5.
Thomassin-Naggara I, Belghitti M, Milon A, Abdel Wahab C, Sadowski E, Rockall A
Eur Radiol
. 2021 May;
31(12):9588-9599.
PMID: 34041567
Objective: To retrospectively review the causes of categorization errors using O-RADS-MRI score and to determine the presumptive causes of these misclassifications. Methods: EURAD database was retrospectively queried to identify misclassified...
6.
Lavdas I, Glocker B, Rueckert D, Taylor S, Aboagye E, Rockall A
Clin Radiol
. 2019 Feb;
74(5):346-356.
PMID: 30803815
Machine learning is now being increasingly employed in radiology to assist with tasks such as automatic lesion detection, segmentation, and characterisation. We are currently involved in an National Institute of...
7.
Sadowski E, Rockall A, Maturen K, Robbins J, Thomassin-Naggara I
Diagn Interv Imaging
. 2018 Sep;
100(10):635-646.
PMID: 30177450
Adnexal lesions are routinely encountered in general practice. Ultrasound is the first line of investigation in determining the benign or malignant potential of an adnexal lesion. In the cases of...
8.
Lavdas I, Rockall A, Daulton E, Kozlowski K, Honeyfield L, Aboagye E, et al.
Clin Radiol
. 2018 May;
73(9):832.e9-832.e16.
PMID: 29793720
Aim: To evaluate apparent diffusion coefficient (ADC) histogram analysis parameters, acquired from whole-body diffusion-weighted magnetic resonance imaging (DW-MRI), as very early predictors of response to chemotherapy in patients with metastatic...
9.
Bazot M, Bharwani N, Huchon C, Kinkel K, Cunha T, Guerra A, et al.
Eur Radiol
. 2016 Dec;
27(7):2765-2775.
PMID: 27921160
Key Points: • This report provides guidelines for MRI in endometriosis. • Minimal and optimal MRI acquisition protocols are provided. • Recommendations are proposed for patient preparation, best MRI sequences...
10.
Tang Y, Benardin L, Booth T, Miquel M, Dilks P, Sahdev A, et al.
Br J Radiol
. 2014 Sep;
87(1043):20130730.
PMID: 25237836
Objective: Semi-quantitative dynamic contrast-enhanced MRI (DCE MRI) has proven useful in discriminating benign from borderline/malignant adnexal lesions. Our aim was to assess if the use of a lesion-to-internal-reference ratio improved...