Improved Automated Quality Control of Skeletal Wrist Radiographs Using Deep Multitask Learning.
Radiographic quality control is an integral component of the radiology workflow. In this study, we developed a convolutional neural network model tailored for automated quality control, specifically designed to detect and classify key attributes of wrist radiographs including projection, laterality...
| Publicado en: | Journal of Imaging Informatics in Medicine Vol. 38; no. 2; pp. 838 - 850 |
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| Autores principales: | , , , , |
| Formato: | diagnostic images research tables/charts Journal Article |
| Publicado: |
Springer Nature
Apr2025
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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=184081727&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 184081727 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 29482925 NR3A jtl: Journal of Imaging Informatics in Medicine issn: 29482925 maglogo: N pubinfo: dt: Apr2025 vid: 38 iid: 2 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 184081727 184081727 184081727 10.1007/s10278-024-01220-9 184081727 ppf: 838 ppct: 12 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Improved Automated Quality Control of Skeletal Wrist Radiographs Using Deep Multitask Learning. aug: au: Hembroff, Guy Klochko, Chad Craig, Joseph Changarnkothapeecherikkal, Harikrishnan Loi, Richard Q. affil: https://ror.org/0036rpn28 Department of Applied Computing, Michigan Technological University, 1400 Townsend Drive, 49931, Houghton, MI, USA sug: subj: Wrist Radiography Muscle, Skeletal Radiography Radiographic Image Enhancement Methods Image Processing, Computer Assisted Quality Improvement Deep Learning Human Convolutional Neural Networks Descriptive Statistics Machine Learning ab: Radiographic quality control is an integral component of the radiology workflow. In this study, we developed a convolutional neural network model tailored for automated quality control, specifically designed to detect and classify key attributes of wrist radiographs including projection, laterality (based on the right/left marker), and the presence of hardware and/or casts. The model's primary objective was to ensure the congruence of results with image requisition metadata to pass the quality assessment. Using a dataset of 6283 wrist radiographs from 2591 patients, our multitask-capable deep learning model based on DenseNet 121 architecture achieved high accuracy in classifying projections (F1 Score of 97.23%), detecting casts (F1 Score of 97.70%), and identifying surgical hardware (F1 Score of 92.27%). The model's performance in laterality marker detection was lower (F1 Score of 82.52%), particularly for partially visible or cut-off markers. This paper presents a comprehensive evaluation of our model's performance, highlighting its strengths, limitations, and the challenges encountered during its development and implementation. Furthermore, we outline planned future research directions aimed at refining and expanding the model's capabilities for improved clinical utility and patient care in radiographic quality control. pubtype: Academic Journal doctype: diagnostic images research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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