Machine Learning for Automation of Radiology Protocols for Quality and Efficiency Improvement.
Purpose: The aim of this study was to enhance multispecialty CT and MRI protocol assignment quality and efficiency through development, testing, and proposed workflow design of a natural language processing (NLP)-based machine learning classifier.Methods: NLP-based machine learning classification mo...
| Publicado en: | Journal of the American College of Radiology Vol. 17; no. 9; pp. 1149 - 1159 |
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| Autores principales: | , , , |
| Formato: | research tables/charts Journal Article |
| Publicado: |
Elsevier B.V.
Sep2020
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| 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=145496860&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 145496860 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 15461440 EAO jtl: Journal of the American College of Radiology issn: 15461440 maglogo: N pubinfo: dt: Sep2020 vid: 17 iid: 9 pid: 467 pub: Elsevier B.V. place: New York, New York artinfo: ui: 145496860 145496860 NLM32278847 145496860 10.1016/j.jacr.2020.03.012 NLM32278847 145496860 ppf: 1149 ppct: 10 formats: tig: atl: Machine Learning for Automation of Radiology Protocols for Quality and Efficiency Improvement. aug: au: Kalra, Angad Chakraborty, Amit Fine, Benjamin Reicher, Joshua affil: Department of Computer Science, University of Toronto, Toronto, Ontario, Canada sug: subj: Automation Specialties, Medical Natural Language Processing Human ab: Purpose: The aim of this study was to enhance multispecialty CT and MRI protocol assignment quality and efficiency through development, testing, and proposed workflow design of a natural language processing (NLP)-based machine learning classifier.Methods: NLP-based machine learning classification models were developed using order entry input data and radiologist-assigned protocols from more than 18,000 unique CT and MRI examinations obtained during routine clinical use. k-Nearest neighbor, random forest, and deep neural network classification models were evaluated at baseline and after applying class frequency and confidence thresholding techniques. To simulate performance in real-world deployment, the model was evaluated in two operating modes in combination: automation (automated assignment of the top result) and clinical decision support (CDS; top-three protocol suggestion for clinical review). Finally, model-radiologist discordance was subjectively reviewed to guide explainability and safe use.Results: Baseline protocol assignment performance achieved weighted precision of 0.757 to 0.824. Simulating real-world deployment using combined thresholding techniques, the optimized deep neural network model assigned 69% of protocols in automation mode with 95% accuracy. In the remaining 31% of cases, the model achieved 92% accuracy in CDS mode. Analysis of discordance with subspecialty radiologist labels revealed both more and less appropriate model predictions.Conclusions: A multiclass NLP-based classification algorithm was designed to drive local operational improvement in CT and MR radiology protocol assignment at subspecialist quality. The results demonstrate a simulated workflow deployment enabling automated assignment of protocols in nearly 7 of 10 cases with very few errors combined with top-three CDS for remaining cases supporting a high-quality, efficient radiology workflow. pubtype: Academic Journal doctype: research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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