Assessment of hip displacement in children with cerebral palsy using machine learning approach.
Manual measurements of migration percentage (MP) on pelvis radiographs for assessing hip displacement are subjective and time consuming. A deep learning approach using convolution neural networks (CNNs) to automatically measure the MP was proposed. The pre-trained Inception ResNet v2 was fine tuned...
| Publicado en: | Medical & Biological Engineering & Computing Vol. 59; no. 9; pp. 1877 - 1888 |
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| Autores principales: | , , , , |
| Formato: | Journal Article |
| Publicado: |
Springer Nature
Sep2021
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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=152044052&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 152044052 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01400118 PO0 jtl: Medical & Biological Engineering & Computing issn: 01400118 maglogo: N pubinfo: dt: Sep2021 vid: 59 iid: 9 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 152044052 151771267 152044052 NLM34357510 10.1007/s11517-021-02416-9 NLM34357510 152044052 ppf: 1877 ppct: 11 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Assessment of hip displacement in children with cerebral palsy using machine learning approach. aug: au: Pham, Thanh-Tu Le, Minh-Binh Le, Lawrence H. Andersen, John Lou, Edmond affil: Department of Radiology and Diagnostic Imaging, University of Alberta, Edmonton, AB, Canada sug: subj: Cerebral Palsy Hip Dislocation Reproducibility of Results Radiography Child Child: 6-12 years ab: Manual measurements of migration percentage (MP) on pelvis radiographs for assessing hip displacement are subjective and time consuming. A deep learning approach using convolution neural networks (CNNs) to automatically measure the MP was proposed. The pre-trained Inception ResNet v2 was fine tuned to detect locations of the eight reference landmarks used for MP measurements. A second network, fine-tuned MobileNetV2, was trained on the regions of interest to obtain more precise landmarks' coordinates. The MP was calculated from the final estimated landmarks' locations. A total of 122 radiographs were divided into 57 for training, 10 for validation, and 55 for testing. The mean absolute difference (MAD) and intra-class correlation coefficient (ICC [2,1]) of the comparison for the MP on 110 measurements (left and right hips) were 4.5 [Formula: see text] 4.3% (95% CI, 3.7-5.3%) and 0.91, respectively. Sensitivity and specificity were 87.8% and 93.4% for the classification of hip displacement (MP-threshold of 30%), and 63.2% and 94.5% for the classification of surgery-needed hips (MP-threshold of 40%). The prediction results were returned within 5 s. The developed fine-tuned CNNs detected the landmarks and provided automatic MP measurements with high accuracy and excellent reliability, which can assist clinicians to diagnose hip displacement in children with CP. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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