Accepted for/Published in: JMIR Research Protocols
Date Submitted: May 13, 2022
Open Peer Review Period: May 13, 2022 - Jul 8, 2022
Date Accepted: Jul 8, 2022
(closed for review but you can still tweet)
Developing an Artificial Intelligence model for reading chest X-rays: protocol for a prospective validation study
ABSTRACT
Background:
Chest X-rays are the most commonly used type of X-rays today, accounting for up to 26% of all radiographic tests performed. However, chest radiography is a complex imaging modality to interpret; several studies have reported discrepancies in chest X-ray interpretations among emergency physicians and radiologists. It is of vital importance to be able to offer a fast and reliable diagnosis for this kind of X-ray, using artificial intelligence (AI) to support the clinician. Oxipit has developed an AI algorithm for reading chest X-rays, available through a web platform called ChestEye. This platform is an automatic computer-aided diagnosis (CAD) system where a reading of the inserted chest X-ray is performed and an automatic report is returned with a capacity to detect 75 pathologies, covering 90% of diagnoses.
Objective:
The overall objective of the study is to perform a validation with prospective data of the ChestEye algorithm as a diagnostic aid. We wish to validate the algorithm for a single pathology and multiple pathologies by evaluating the accuracy, sensitivity and specificity of the algorithm.
Methods:
A prospective study will be carried out to compare the diagnosis of the reference radiologist for the users attending the primary care centre in the Osona region (Spain), with the diagnosis of the ChestEye AI algorithm.
Results:
Patient recruitment began in February 2022 on a rolling basis until the target sample was reached. It is hoped to obtain sufficient evidence to demonstrate that the use of AI in the reading of chest X-rays can be a good tool for diagnostic support. However, there is a decreasing number of radiology professionals and, therefore, it is necessary to develop and validate tools to support professionals who have to interpret these tests.
Conclusions:
If the results of the validation of the model are satisfactory, it could be implemented as a support tool and allow an increase in the accuracy and speed of diagnosis, patient safety and agility in the primary care system, and reduce the costs of unnecessary tests. Clinical Trial: 21/288
Citation
Request queued. Please wait while the file is being generated. It may take some time.
Copyright
© The authors. All rights reserved. This is a privileged document currently under peer-review/community review (or an accepted/rejected manuscript). Authors have provided JMIR Publications with an exclusive license to publish this preprint on it's website for review and ahead-of-print citation purposes only. While the final peer-reviewed paper may be licensed under a cc-by license on publication, at this stage authors and publisher expressively prohibit redistribution of this draft paper other than for review purposes.