Development and Application of a Web-Based Platform for Assessment of Observer Performance in Medical Imaging

Borgbjerg, Jens

doi:10.54337/aau495541953

Cited by 1 publication

(1 citation statement)

References 72 publications

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“…We customized an existing pure web-based DICOM viewer with a high usability score. 13,14 This viewer incorporates typical PACS viewer functionalities and is written in HTML5/Javascript, incorporating jQuery UI (https://jqueryui.com/) for user interface and interaction. The viewer accepts imaging studies in the DICOM format.…”

Section: Web-based Dicom Viewer Integrationmentioning

confidence: 99%

Towards AI-augmented radiology education: a web-based application for perception training in chest X-ray nodule detection

Borgbjerg,

Thompson,

Salte

et al. 2023

The British Journal of Radiology

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Objectives: Artificial intelligence (AI)-based applications for augmenting radiological education are underexplored. Prior studies have demonstrated the effectiveness of simulation in radiological perception training. This study aimed to develop and make available a pure web-based application called Perception Trainer for perception training in lung nodule detection in chest X-rays. Methods: Based on open-access data, we trained a deep-learning model for lung segmentation in chest X-rays. Subsequently, an algorithm for artificial lung nodule generation was implemented and combined with the segmentation model to allow on-the-fly procedural insertion of lung nodules in chest X-rays. This functionality was integrated into an existing zero-footprint web-based DICOM viewer, and a dynamic HTML page was created to specify case generation parameters. Results: The result is an easily accessible platform-agnostic web application available at: https://castlemountain.dk/mulrecon/perceptionTrainer.html. The application allows the user to specify the characteristics of lung nodules to be inserted into chest X-rays, and it produces automated feedback regarding nodule detection performance. Generated cases can be shared through a uniform resource locator. Conclusion: We anticipate that the description and availability of our developed solution with open-sourced codes may help facilitate radiological education and stimulate the development of similar AI-augmented educational tools. Advances in knowledge: A web-based application applying artificial intelligence-based techniques for radiological perception training was developed. The application demonstrates a novel approach for on-the-fly generation of cases in chest x-ray lung nodule detection employing deep learning-based segmentation and lung nodule simulation.

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Section: Web-based Dicom Viewer Integrationmentioning

confidence: 99%

Towards AI-augmented radiology education: a web-based application for perception training in chest X-ray nodule detection

Borgbjerg,

Thompson,

Salte

et al. 2023

The British Journal of Radiology

View full text Add to dashboard Cite

show abstract

Development and Application of a Web-Based Platform for Assessment of Observer Performance in Medical Imaging

Abstract: Ultra-low-dose non-contrast CT is an adequate replacement for CT angiography in the assessment of maximal abdominal aortic diameter -[resubmitted (major revision) to Acta Radiologica Open]

Cited by 1 publication

References 72 publications

Towards AI-augmented radiology education: a web-based application for perception training in chest X-ray nodule detection

Towards AI-augmented radiology education: a web-based application for perception training in chest X-ray nodule detection

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