A Patch-Based Deep Learning Approach for Detecting Rib Fractures on Frontal Radiographs in Young Children.
Chest radiography is the modality of choice for the identification of rib fractures in young children and there is value for the development of computer-aided rib fracture detection in this age group. However, the automated identification of rib fractures on chest radiographs can be challenging due...
| Published in: | Journal of Digital Imaging Vol. 36; no. 4; pp. 1302 - 1314 |
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| Main Authors: | , , , , , , , , , |
| Format: | diagnostic images research tables/charts Journal Article |
| Published: |
Springer Nature
Aug2023
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| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=169808791&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 169808791 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: 169808791 162316506 169808791 169808791 10.1007/s10278-023-00793-1 169808791 ppf: 1302 ppct: 12 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: A Patch-Based Deep Learning Approach for Detecting Rib Fractures on Frontal Radiographs in Young Children. aug: au: Ghosh, Adarsh Patton, Daniella Bose, Saurav Henry, M. Katherine Ouyang, Minhui Huang, Hao Vossough, Arastoo Sze, Raymond Sotardi, Susan Francavilla, Michael affil: Department of Radiology, Children's Hospital of Philadelphia, Philadelphia, PA, USA sug: subj: Rib Fractures Radiography Deep Learning Image Processing, Computer Assisted Methods Radiography, Thoracic Diagnosis, Computer Assisted Algorithms Human Infant, Newborn Infant Neural Networks (Computer) ROC Curve Descriptive Statistics Sensitivity and Specificity Automation Infant, Newborn: birth-1 month Infant: 1-23 months ab: Chest radiography is the modality of choice for the identification of rib fractures in young children and there is value for the development of computer-aided rib fracture detection in this age group. However, the automated identification of rib fractures on chest radiographs can be challenging due to the need for high spatial resolution in deep learning frameworks. A patch-based deep learning algorithm was developed to automatically detect rib fractures on frontal chest radiographs in children under 2 years old. A total of 845 chest radiographs of children 0–2 years old (median: 4 months old) were manually segmented for rib fractures by radiologists and served as the ground-truth labels. Image analysis utilized a patch-based sliding-window technique, to meet the high-resolution requirements for fracture detection. Standard transfer learning techniques used ResNet-50 and ResNet-18 architectures. Area-under-curve for precision-recall (AUC-PR) and receiver-operating-characteristic (AUC-ROC), along with patch and whole-image classification metrics, were reported. On the test patches, the ResNet-50 model showed AUC-PR and AUC-ROC of 0.25 and 0.77, respectively, and the ResNet-18 showed an AUC-PR of 0.32 and AUC-ROC of 0.76. On the whole-radiograph level, the ResNet-50 had an AUC-ROC of 0.74 with 88% sensitivity and 43% specificity in identifying rib fractures, and the ResNet-18 had an AUC-ROC of 0.75 with 75% sensitivity and 60% specificity in identifying rib fractures. This work demonstrates the utility of patch-based analysis for detection of rib fractures in children under 2 years old. Future work with large cohorts of multi-institutional data will improve the generalizability of these findings to patients with suspicion of child abuse. pubtype: Academic Journal doctype: diagnostic images research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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