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.

Study Overview

• Status: Completed
• Conditions: Testing a Computational Model to Estimate Patient Size
• Intervention / Treatment: Other: A single measurement of the patient's abdominal depth

Detailed Description

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.

• Study Type: Observational
• Enrollment (Actual): 20

Contacts and Locations

Study Locations

• United Kingdom
  • Angus
    • Dundee, Angus, United Kingdom, DD1 9SY
      • NHS Tayside

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: 17 years and older (Child, Adult, Older Adult)
• Accepts Healthy Volunteers: No
• Genders Eligible for Study: All

• Sampling Method: Non-Probability Sample

Study Population

Ambulatory outpatients attending Ninewells Hospital's radiology department for an anterior-posterior abdomen x-ray examination

Description

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

Study Plan

How is the study designed?

Number of groups / cohorts

1

Cohorts and Interventions

Group / Cohort	Intervention / Treatment
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.	Other: A single measurement of the patient's abdominal depth; A single measurement of the patient's anterior-posterior abdominal depth

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
Accuracy of the Computational Model	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

Collaborators and Investigators

• Sponsor: NHS Tayside
• Collaborators: University of Dundee
• Investigators: Study Chair: Sarah Vinnicombe, MD, University of Dundee

Study record dates

Study Major Dates

• Study Start (Actual): March 13, 2018
• Primary Completion (Actual): May 7, 2018
• Study Completion (Actual): August 24, 2018

Study Registration Dates

• First Submitted: October 31, 2017
• First Submitted That Met QC Criteria: November 13, 2017
• First Posted (Actual): November 14, 2017

Study Record Updates

• Last Update Posted (Actual): June 11, 2019
• Last Update Submitted That Met QC Criteria: June 10, 2019
• Last Verified: June 1, 2019

More Information

Terms related to this study

• Other Study ID Numbers: 2017RA01

Plan for Individual participant data (IPD)

• Plan to Share Individual Participant Data (IPD)?: NO

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: No