Estimating Patient Size From a Single Radiograph (VocMepAdar)
Validation of a Computational Model to Estimate Patient Anterior-posterior Dimension From an Abdominal Radiograph
A computational model has been created to estimate the abdominal depth of a patient from a single x-ray image. The model has been tested using phantoms and found to be accurate; this study aims to test the accuracy of the model with patients and in a clinical setting.
This will be achieved by enrolling patient's who have already been referred for an anterior-posterior abdomen x-ray examination to the trial, taking a physical measurement of their anterior-posterior abdominal depth and then comparing this measured value with a value as estimated using the computational model based on the patient's x-ray image.
Studienübersicht
Status
Intervention / Behandlung
Detaillierte Beschreibung
A non-commercial computational model has been developed in-house to estimate the patient's anterior-posterior or lateral depth using the radiographic image and the known exposure factors with which it was undertaken. This model has been tested using single composition phantoms and found to be accurate. If it was found to be accurate for real clinical examinations, this would automate the measurement of patient size and give institutions the estimate of patient size required for local paediatric patient dose audit. In turn, this would provide the national data required to propose national reference values for paediatric x-ray examinations, which would give all institutions an important comparator for their performance. This would lead to optimisation in those sites most requiring it; nationally, paediatric x-ray imaging would improve in time.
This pilot study is necessary to determine if the computational model is accurate enough to be relied upon. Accuracy will be determined by comparing the estimate made by the computational model for each patient with an actual measurement of the patient's anterior-posterior abdomen depth made at the time of the examination.
Studientyp
Einschreibung (Tatsächlich)
Kontakte und Standorte
Studienorte
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Angus
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Dundee, Angus, Vereinigtes Königreich, DD1 9SY
- NHS Tayside
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Teilnahmekriterien
Zulassungskriterien
Studienberechtigtes Alter
Akzeptiert gesunde Freiwillige
Studienberechtigte Geschlechter
Probenahmeverfahren
Studienpopulation
Beschreibung
Inclusion Criteria:
- Adult
- Referred to Ninewells Hospital for an anterior-posterior abdomen x-ray examination
Exclusion Criteria:
- Patients unable to give consent
- Patients who have had a contrast injection in the previous 24 hours
- Patients suffering abdominal pain at the time of the examination
Studienplan
Wie ist die Studie aufgebaut?
Designdetails
Kohorten und Interventionen
Gruppe / Kohorte |
Intervention / Behandlung |
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Patient cohort
20 patients referred to Ninewells Hospital radiology department for an anterior-posterior abdomen x-ray examination.
All of these patients will have a measurement of their anterior-posterior depth before undergoing their x-ray examination.
An estimate of their anterior-posterior depth will then be made from their x-ray image using the computational model.
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A single measurement of the patient's anterior-posterior abdominal depth
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Was misst die Studie?
Primäre Ergebnismessungen
Ergebnis Maßnahme |
Maßnahmenbeschreibung |
Zeitfenster |
|---|---|---|
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Accuracy of the Computational Model
Zeitfenster: 2 months
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The computational model was used to estimate the patient's anterior-posterior abdominal depth using the digital radiographic image, the exposure factors with which it was acquired and a priori knowledge relating to the x-ray unit and digital detector. The outcome measure was the accuracy with which the computational model estimates the patient's anterior-posterior abdominal depth. It was determined by comparing the estimate to measured anterior-posterior abdominal depth (measured at the time of the x-ray examination). Results are expressed as a percentage deviation; a low % deviation is more accurate, a high % deviation less accurate. |
2 months
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Mitarbeiter und Ermittler
Sponsor
Mitarbeiter
Ermittler
- Studienstuhl: Sarah Vinnicombe, MD, University of Dundee
Studienaufzeichnungsdaten
Haupttermine studieren
Studienbeginn (Tatsächlich)
Primärer Abschluss (Tatsächlich)
Studienabschluss (Tatsächlich)
Studienanmeldedaten
Zuerst eingereicht
Zuerst eingereicht, das die QC-Kriterien erfüllt hat
Zuerst gepostet (Tatsächlich)
Studienaufzeichnungsaktualisierungen
Letztes Update gepostet (Tatsächlich)
Letztes eingereichtes Update, das die QC-Kriterien erfüllt
Zuletzt verifiziert
Mehr Informationen
Begriffe im Zusammenhang mit dieser Studie
Andere Studien-ID-Nummern
- 2017RA01
Plan für individuelle Teilnehmerdaten (IPD)
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Studiert ein von der US-amerikanischen FDA reguliertes Geräteprodukt
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