Automated detection of IVC filters on radiographs with deep convolutional neural networks.
Purpose: To create an algorithm able to accurately detect IVC filters on radiographs without human assistance, capable of being used to screen radiographs to identify patients needing IVC filter retrieval. Methods: A primary dataset of 5225 images, 30% of which included IVC filters, was assembled an...
| Publicado en: | Abdominal Radiology Vol. 48; no. 2; pp. 758 - 765 |
|---|---|
| Autores principales: | , , , , , |
| Formato: | Journal Article |
| Publicado: |
Springer Nature
Feb2023
|
| 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=161717333&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 161717333 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 2366004X JT14 jtl: Abdominal Radiology issn: 2366004X maglogo: N pubinfo: dt: Feb2023 vid: 48 iid: 2 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 161717333 160142326 10.1007/s00261-022-03734-8 161717333 ppf: 758 ppct: 7 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Automated detection of IVC filters on radiographs with deep convolutional neural networks. aug: au: Mongan, John Kohli, Marc D. Houshyar, Roozbeh Chang, Peter D. Glavis-Bloom, Justin Taylor, Andrew G. affil: Department of Radiology and Biomedical Imaging, Center for Intelligent Imaging, University of California San Francisco, 505 Parnassus Avenue, 94143-0628, San Francisco, CA, USA sug: ab: Purpose: To create an algorithm able to accurately detect IVC filters on radiographs without human assistance, capable of being used to screen radiographs to identify patients needing IVC filter retrieval. Methods: A primary dataset of 5225 images, 30% of which included IVC filters, was assembled and annotated. 85% of the data was used to train a Cascade R-CNN (Region Based Convolutional Neural Network) object detection network incorporating a pre-trained ResNet-50 backbone. The remaining 15% of the data, independently annotated by three radiologists, was used as a test set to assess performance. The algorithm was also assessed on an independently constructed 1424-image dataset, drawn from a different institution than the primary dataset. Results: On the primary test set, the algorithm achieved a sensitivity of 96.2% (95% CI 92.7–98.1%) and a specificity of 98.9% (95% CI 97.4–99.5%). Results were similar on the external test set: sensitivity 97.9% (95% CI 96.2–98.9%), specificity 99.6 (95% CI 98.9–99.9%). Conclusion: Fully automated detection of IVC filters on radiographs with high sensitivity and excellent specificity required for an automated screening system can be achieved using object detection neural networks. Further work will develop a system for identifying patients for IVC filter retrieval based on this algorithm. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
|---|