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Open Access 26-12-2022 | Computed Tomography | Imaging Informatics and Artificial Intelligence

Artificial intelligence-based diagnosis of asbestosis: analysis of a database with applicants for asbestosis state aid

Authors: Kevin B. W. Groot Lipman, Cornedine J. de Gooijer, Thierry N. Boellaard, Ferdi van der Heijden, Regina G. H. Beets-Tan, Zuhir Bodalal, Stefano Trebeschi, Jacobus A. Burgers

Published in: European Radiology | Issue 5/2023

Abstract

Objectives

In many countries, workers who developed asbestosis due to their occupation are eligible for government support. Based on the results of clinical examination, a team of pulmonologists determine the eligibility of patients to these programs. In this Dutch cohort study, we aim to demonstrate the potential role of an artificial intelligence (AI)-based system for automated, standardized, and cost-effective evaluation of applications for asbestosis patients.

Methods

A dataset of n = 523 suspected asbestosis cases/applications from across the Netherlands was retrospectively collected. Each case/application was reviewed, and based on the criteria, a panel of three pulmonologists would determine eligibility for government support. An AI system is proposed, which uses thoracic CT images as input, and predicts the assessment of the clinical panel. Alongside imaging, we evaluated the added value of lung function parameters.

Results

The proposed AI algorithm reached an AUC of 0.87 (p < 0.001) in the prediction of accepted versus rejected applications. Diffusion capacity (DLCO) also showed comparable predictive value (AUC = 0.85, p < 0.001), with little correlation between the two parameters (r-squared = 0.22, p < 0.001). The combination of the imaging AI score and DLCO achieved superior performance (AUC = 0.95, p < 0.001). Interobserver variability between pulmonologists on the panel was estimated at alpha = 0.65 (Krippendorff’s alpha).

Conclusion

We developed an AI system to support the clinical decision-making process for the application to the government support for asbestosis. A multicenter prospective validation study is currently ongoing to examine the added value and reliability of this system alongside the clinic panel.

Key Points

• Artificial intelligence can detect imaging patterns of asbestosis in CT scans in a cohort of patients applying for state aid.

• Combining the AI prediction with the diffusing lung function parameter reaches the highest diagnostic performance.

• Specific cases with fibrosis but no asbestosis were correctly classified, suggesting robustness of the AI system, which is currently under prospective validation.

Available only for authorised users

https://github.com/nki-radiology/asbestosis

https://github.com/lalonderodney/SegCaps

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Title: Artificial intelligence-based diagnosis of asbestosis: analysis of a database with applicants for asbestosis state aid
Authors: Kevin B. W. Groot Lipman
Cornedine J. de Gooijer
Thierry N. Boellaard
Ferdi van der Heijden
Regina G. H. Beets-Tan
Zuhir Bodalal
Stefano Trebeschi
Jacobus A. Burgers
Publication date: 26-12-2022
Publisher: Springer Berlin Heidelberg
Keywords: Computed Tomography
Computed Tomography
Artificial Intelligence
Asbestosis
Published in: European Radiology / Issue 5/2023
Print ISSN: 0938-7994
Electronic ISSN: 1432-1084
DOI: https://doi.org/10.1007/s00330-022-09304-2

Keynote webinar | Spotlight on medication adherence

Springer Medicine

Artificial intelligence-based diagnosis of asbestosis: analysis of a database with applicants for asbestosis state aid

Abstract

Objectives

Methods

Results

Conclusion

Key Points

Keynote webinar | Spotlight on medication adherence

Springer Medicine

Abstract

Objectives

Methods

Results

Conclusion

Key Points

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