Automated detection and classification of shoulder arthroplasty models using deep learning.
Objective: To develop and evaluate the performance of deep convolutional neural networks (DCNN) to detect and identify specific total shoulder arthroplasty (TSA) models.Materials and Methods: We included 482 radiography studies obtained from publicly available image repositories with native shoulder...
| Published in: | Skeletal Radiology Vol. 49; no. 10; pp. 1623 - 1633 |
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| Main Authors: | , , , , , , |
| Format: | research tables/charts Journal Article |
| Published: |
Springer Nature
Oct2020
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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=145271762&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 145271762 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 03642348 O14 jtl: Skeletal Radiology issn: 03642348 maglogo: N pubinfo: dt: Oct2020 vid: 49 iid: 10 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 145271762 144020701 145271762 NLM32415371 145271762 10.1007/s00256-020-03463-3 NLM32415371 145271762 ppf: 1623 ppct: 10 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Automated detection and classification of shoulder arthroplasty models using deep learning. aug: au: Yi, Paul H. Kim, Tae Kyung Wei, Jinchi Li, Xinning Hager, Gregory D. Sair, Haris I. Fritz, Jan affil: The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, USA sug: subj: ROC Curve Radiography Human ab: Objective: To develop and evaluate the performance of deep convolutional neural networks (DCNN) to detect and identify specific total shoulder arthroplasty (TSA) models.Materials and Methods: We included 482 radiography studies obtained from publicly available image repositories with native shoulders, reverse TSA (RTSA) implants, and five different TSA models. We trained separate ResNet DCNN-based binary classifiers to (1) detect the presence of shoulder arthroplasty implants, (2) differentiate between TSA and RTSA, and (3) differentiate between the five TSA models, using five individual classifiers for each model, respectively. Datasets were divided into training, validation, and test datasets. Training and validation datasets were 20-fold augmented. Test performances were assessed with area under the receiver-operating characteristic curves (AUC-ROC) analyses. Class activation mapping was used to identify distinguishing imaging features used for DCNN classification decisions.Results: The DCNN for the detection of the presence of shoulder arthroplasty implants achieved an AUC-ROC of 1.0, whereas the AUC-ROC for differentiation between TSA and RTSA was 0.97. Class activation map analysis demonstrated the emphasis on the characteristic arthroplasty components in decision-making. DCNNs trained to distinguish between the five TSA models achieved AUC-ROCs ranging from 0.86 for Stryker Solar to 1.0 for Zimmer Bigliani-Flatow with class activation map analysis demonstrating an emphasis on unique implant design features.Conclusion: DCNNs can accurately identify the presence of and distinguish between TSA & RTSA, and classify five specific TSA models with high accuracy. The proof of concept of these DCNNs may set the foundation for an automated arthroplasty atlas for rapid and comprehensive model identification. pubtype: Academic Journal doctype: research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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