Automating Angle Measurements on Foot Radiographs in Young Children: Feasibility and Performance of a Convolutional Neural Network Model.
Measurement of angles on foot radiographs is an important step in the evaluation of malalignment. The objective is to develop a CNN model to measure angles on radiographs, using radiologists' measurements as the reference standard. This IRB-approved retrospective study included 450 radiographs from...
| Publicado en: | Journal of Digital Imaging Vol. 36; no. 4; pp. 1419 - 1431 |
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| Autores principales: | , , , , , , , , , , , |
| Formato: | diagnostic images equations & formulas research tables/charts Journal Article |
| Publicado: |
Springer Nature
Aug2023
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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=169808819&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 169808819 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 08971889 DOQ jtl: Journal of Digital Imaging issn: 08971889 maglogo: N pubinfo: dt: Aug2023 vid: 36 iid: 4 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 169808819 163332771 169808819 169808819 10.1007/s10278-023-00824-x 169808819 ppf: 1419 ppct: 12 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Automating Angle Measurements on Foot Radiographs in Young Children: Feasibility and Performance of a Convolutional Neural Network Model. aug: au: Patton, Daniella Ghosh, Adarsh Farkas, Amy Sotardi, Susan Francavilla, Michael Venkatakrishna, Shyam Bose, Saurav Ouyang, Minhui Huang, Hao Davidson, Richard Sze, Raymond Nguyen, Jie affil: Department of Radiology, Children's Hospital of Philadelphia, Philadelphia, PA, USA sug: subj: Convolutional Neural Networks Evaluation Foot Radiography Diagnosis, Computer Assisted Pediatric Care Human Child Pilot Studies Retrospective Design Record Review Descriptive Statistics Machine Learning Clubfoot Child: 6-12 years ab: Measurement of angles on foot radiographs is an important step in the evaluation of malalignment. The objective is to develop a CNN model to measure angles on radiographs, using radiologists' measurements as the reference standard. This IRB-approved retrospective study included 450 radiographs from 216 patients (< 3 years of age). Angles were automatically measured by means of image segmentation followed by angle calculation, according to Simon's approach for measuring pediatric foot angles. A multiclass U-Net model with a ResNet-34 backbone was used for segmentation. Two pediatric radiologists independently measured anteroposterior and lateral talocalcaneal and talo-1st metatarsal angles using the test dataset and recorded the time used for each study. Intraclass correlation coefficients (ICC) were used to compare angle and paired Wilcoxon signed-rank test to compare time between radiologists and the CNN model. There was high spatial overlap between manual and CNN-based automatic segmentations with dice coefficients ranging between 0.81 (lateral 1st metatarsal) and 0.94 (lateral calcaneus). Agreement was higher for angles on the lateral view when compared to the AP view, between radiologists (ICC: 0.93–0.95, 0.85–0.92, respectively) and between radiologists' mean and CNN calculated (ICC: 0.71–0.73, 0.41–0.52, respectively). Automated angle calculation was significantly faster when compared to radiologists' manual measurements (3 ± 2 vs 114 ± 24 s, respectively; P < 0.001). A CNN model can selectively segment immature ossification centers and automatically calculate angles with a high spatial overlap and moderate to substantial agreement when compared to manual methods, and 39 times faster. pubtype: Academic Journal doctype: diagnostic images equations & formulas research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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