Developing an Artificial Intelligence Model for Reading Chest X-rays: Protocol for a Prospective Validation Study

Queralt Miró Catalina; Aïna Fuster-Casanovas; Jordi Solé-Casals; Josep Vidal-Alaball

doi:10.2196/39536

Developing an Artificial Intelligence Model for Reading Chest X-rays: Protocol for a Prospective Validation Study

JMIR Res Protoc. 2022 Nov 16;11(11):e39536. doi: 10.2196/39536.

Authors

Queralt Miró Catalina^{1

2}, Aïna Fuster-Casanovas^{1

2}, Jordi Solé-Casals^{3

4}, Josep Vidal-Alaball^{1

2

5}

Affiliations

¹ Unitat de Suport a la Recerca de la Catalunya Central, Fundació Institut Universitari per a la Recerca a l'Atenció Primària de Salut Jordi Gol i Gurina, Sant Fruitós de Bages, Spain.
² Health Promotion in Rural Areas Research Group, Gerencia Territorial de la Catalunya Central, Institut Català de la Salut, Sant Fruitós de Bages, Spain.
³ Data and Signal Processing group, Faculty of Science, Technology and Engineering, University of Vic-Central University of Catalonia, Vic, Spain.
⁴ Department of Psychiatry, University of Cambridge, Cambridge, United Kingdom.
⁵ Faculty of Medicine, University of Vic-Central University of Catalonia, Vic, Spain.

PMID: 36383419
PMCID: PMC9713620
DOI: 10.2196/39536

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 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 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 validation study will be carried out to compare the diagnosis of the reference radiologists for the users attending the primary care center in the Osona region (Spain), with the diagnosis of the ChestEye AI algorithm. Anonymized chest x-ray images will be acquired and fed into the AI algorithm interface, which will return an automatic report. A radiologist will evaluate the same chest x-ray, and both assessments will be compared to calculate the precision, sensitivity, specificity, and accuracy of the AI algorithm. Results will be represented globally and individually for each pathology using a confusion matrix and the One-vs-All methodology.

Results: Patient recruitment was conducted from February 7, 2022, and it is expected that data can be obtained in 5 to 6 months. In June 2022, more than 450 x-rays have been collected, so it is expected that 600 samples will be gathered in July 2022. We hope 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, while reducing the cost of unnecessary tests.

International registered report identifier (irrid): PRR1-10.2196/39536.

Keywords: artificial intelligence; chest x-ray; machine learning; radiology; validation.

©Queralt Miró Catalina, Aïna Fuster-Casanovas, Jordi Solé-Casals, Josep Vidal-Alaball. Originally published in JMIR Research Protocols (https://www.researchprotocols.org), 16.11.2022.