Estimating Patient Size From a Single Radiograph

Study Overview
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Brief Summary
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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.

Detailed Description
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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.

Official Title
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Validation of a Computational Model to Estimate Patient Anterior-posterior Dimension From an Abdominal Radiograph

Conditions
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Testing a Computational Model to Estimate Patient Size

Intervention / Treatment
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* Other: A single measurement of the patient's abdominal depth


Participation Criteria
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Eligibility Criteria
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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

Ages Eligible for Study
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Minimum Age: 17 Years

Sexes Eligible for Study
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All

Accepts Healthy Volunteers
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No

Study Plan
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How is the study designed?
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Arms and Interventions

| Participant Group/Arm | Intervention/Treatment |
| --- | --- |
| Patient cohort<br>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<br>* A single measurement of the patient's anterior-posterior abdominal depth<br>|

What is the study measuring?
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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 |