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Potential of a machine-learning model for dose optimization in CT quality assurance

  • Chest
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Abstract

Objectives

To evaluate machine learning (ML) to detect chest CT examinations with dose optimization potential for quality assurance in a retrospective, cross-sectional study.

Methods

Three thousand one hundred ninety-nine CT chest examinations were used for training and testing of the feed-forward, single hidden layer neural network (January 2016–December 2017, 60% male, 62 ± 15 years, 80/20 split). The model was optimized and trained to predict the volumetric computed tomography dose index (CTDIvol) based on scan patient metrics (scanner, study description, protocol, patient age, sex, and water-equivalent diameter (DW)). The root mean-squared error (RMSE) was calculated as performance measurement. One hundred separate, consecutive chest CTs were used for validation (January 2018, 60% male, 63 ± 16 years), independently reviewed by two blinded radiologists with regard to dose optimization, and used to define an optimal cutoff for the model.

Results

RMSE was 1.71, 1.45, and 1.52 for the training, test, and validation dataset, respectively. The scanner and DW were the most important features. The radiologists found dose optimization potential in 7/100 of the validation cases. A percentage deviation of 18.3% between predicted and actual CTDIvol was found to be the optimal cutoff: 8/100 cases were flagged as suboptimal by the model (range 18.3–53.2%). All of the cases found by the radiologists were identified. One examination was flagged only by the model.

Conclusions

ML can comprehensively detect CT examinations with dose optimization potential. It may be a helpful tool to simplify CT quality assurance. CT scanner and DW were most important. Final human review remains necessary. A threshold of 18.3% between the predicted and actual CTDIvol seems adequate for CT quality assurance.

Key Points

• Machine learning can be integrated into CT quality assurance to improve retrospective analysis of CT dose data.

• Machine learning may help to comprehensively detect dose optimization potential in chest CT, but an individual review of the results by an experienced radiologist or radiation physicist is required to exclude false-positive findings.

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Abbreviations

CTDIvol :

Volumetric computed tomography dose index

DLP:

Dose length product

DRLs:

Diagnostic reference levels

D W :

Water-equivalent diameter

ML:

Machine learning

QA:

Quality assurance

RMSE:

Root mean-squared error

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Funding

The authors state that this work has not received any funding.

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Correspondence to Christian Rubbert.

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Guarantor

The scientific guarantor of this publication is Johannes Boos.

Conflict of interest

The authors of this manuscript declare relationships with the following companies: AM—Cerner HS Deutschland GmbH (employee) and Pulmokard GmbH (consultant).

Statistics and biometry

One of the authors has significant statistical expertise.

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Written informed consent was waived by the Institutional Review Board.

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Institutional Review Board approval was obtained.

Methodology

• retrospective

• cross-sectional study

• performed at one institution

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Meineke, A., Rubbert, C., Sawicki, L.M. et al. Potential of a machine-learning model for dose optimization in CT quality assurance. Eur Radiol 29, 3705–3713 (2019). https://doi.org/10.1007/s00330-019-6013-6

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  • DOI: https://doi.org/10.1007/s00330-019-6013-6

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