Potential of a machine-learning model for dose optimization in CT quality assurance.
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,...
| Publicado en: | European Radiology Vol. 29; no. 7; pp. 3705 - 3714 |
|---|---|
| Autores principales: | , , , , , , , , , , |
| Formato: | Journal Article |
| Publicado: |
Springer Nature
Jul2019
|
| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=136842400&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 136842400 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 09387994 NPH jtl: European Radiology issn: 09387994 maglogo: N pubinfo: dt: Jul2019 vid: 29 iid: 7 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 136842400 136842400 NLM30783785 10.1007/s00330-019-6013-6 NLM30783785 136842400 ppf: 3705 ppct: 9 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Potential of a machine-learning model for dose optimization in CT quality assurance. aug: au: Meineke, Axel Rubbert, Christian Sawicki, Lino M. Thomas, Christoph Klosterkemper, Yan Appel, Elisabeth Caspers, Julian Bethge, Oliver T. Kröpil, Patric Antoch, Gerald Boos, Johannes affil: Cerner HS Deutschland GmbH, 13629, Berlin, Germany sug: subj: Radiation Injuries Prevention and Control Radiography, Thoracic Standards Quality Assurance Thoracic Diseases Diagnosis Multidetector Computed Tomography Standards Cross Sectional Studies Aged Retrospective Design Female Young Adult Middle Age Adolescence Adult Aged, 80 and Over Radiation Dosage Male Ferrans and Powers Quality of Life Index Scales Aged: 65+ years Middle Aged: 45-64 years Adolescent: 13-18 years Adult: 19-44 years Aged, 80 & over Female Male ab: 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. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
|---|