Evidence map›Paper›PMID 32267728›Full record

ArticleThe British journal of radiology2020

Determining patient abdomen thickness from a single digital radiograph with a computational model: clinical results from a proof of concept study.

Mark Worrall, Sarah Vinnicombe, David Sutton

Open access · bronzeAbstract read
In one paragraph

Article in The British journal of radiology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact, top 96% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed, 0 citations in OpenAlex.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors at 2 institutions in 1 country.

Mark WorrallMedical Physics, Ninewells Hospital and Medical School, Ninewells Avenue, Dundee, Scotland.
Sarah VinnicombeThirlestaine Breast Centre, Cheltenham General Hospital, Cheltenham, England.
David SuttonMedical Physics, Ninewells Hospital and Medical School, Ninewells Avenue, Dundee, Scotland.
Ninewells Hospital · GBCheltenham General Hospital · GB

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectiveA computational model has been created to estimate the abdominal thickness of a patient following an X-ray examination; its intended application is assisting with patient dose audit of paediatric X-ray examinations. This work evaluates the accuracy of the computational model in a clinical setting for adult patients undergoing anteroposterior (AP) abdomen X-ray examinations.

methodsThe model estimates patient thickness using the radiographic image, the exposure factors with which the image was acquired, a priori knowledge of the characteristics of the X-ray unit and detector and the results of extensive Monte Carlo simulation of patient examinations. For 20 patients undergoing AP abdominal X-ray examinations, the model was used to estimate the patient thickness; these estimates were compared against a direct measurement made at the time of the examination.

resultsEstimates of patient thickness made using the model were on average within ±5.8% of the measured thickness.

conclusionThe model can be used to accurately estimate the thickness of a patient undergoing an AP abdominal X-ray examination where the patient's size falls within the range of the size of patients used to create the computational model. ADVANCES IN KNOWLEDGE: This work demonstrates that it is possible to accurately estimate the AP abdominal thickness of an adult patient using the digital X-ray image and a computational model.

Indexed as

Computer SimulationAbdomenAgedAged, 80 and overFemaleHumansMaleMonte Carlo MethodPhantoms, ImagingProof of Concept StudyRadiography, AbdominalTomography, X-Ray Computed

Identifiers

PMID32267728
PMCPMC7336075
OpenAlexW3014220105

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.