ArticleEuropean radiology2019
Potential of a machine-learning model for dose optimization in CT quality assurance.
Article in European radiology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Artificial intelligence and its potential integration with the clinical practice of diagnostic imaging medical physicists: a review.Physical and engineering sciences in medicine · 2025Review
- Patient radiation dose during angiography and embolization for abdominal hemorrhage: the influence of CT angiography, fluoroscopy system, patient and procedural variables.CVIR endovascular · 2022Article
- Image Quality Control in Lumbar Spine Radiography Using Enhanced U-Net Neural Networks.Frontiers in public health · 2022Article
- Utilization of artificial intelligence approach for prediction of DLP values for abdominal CT scans: A high accuracy estimation for risk assessment.Frontiers in public health · 2022Article
- Upstream Machine Learning in Radiology.Radiologic clinics of North America · 2021Review
- ALIGNING VIDEO-AND STRUCTURED DATA FOR IMAGING OPTIMISATION.Radiation protection dosimetry · 2021Article
- Multicentric study of patients receiving 50 or 100 mSv in a single day through CT imaging-frequency determination and imaging protocols involved.European radiology · 2021Article
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Authors and funding
11 authors.
Funding
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Abstract
objectivesTo evaluate machine learning (ML) to detect chest CT examinations with dose optimization potential for quality assurance in a retrospective, cross-sectional study.
methodsThree 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 (CTDI
resultsRMSE was 1.71, 1.45, and 1.52 for the training, test, and validation dataset, respectively. The scanner and D
conclusionsML can comprehensively detect CT examinations with dose optimization potential. It may be a helpful tool to simplify CT quality assurance. CT scanner and D 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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