Automated classification of hip fractures using deep convolutional neural networks with orthopedic surgeon-level accuracy: ensemble decision-making with antero-posterior and lateral radiographs.
Background and purpose — Deep-learning approaches based on convolutional neural networks (CNNs) are gaining interest in the medical imaging field. We evaluated the diagnostic performance of a CNN to discriminate femoral neck fractures, trochanteric fractures, and non-fracture using antero-posterior...
| Publicado en: | Acta Orthopaedica Vol. 91; no. 6; pp. 699 - 705 |
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| Autores principales: | , , , , , , , , , , , , , , , , , |
| Formato: | diagnostic images research tables/charts Journal Article |
| Publicado: |
Medical Journals Sweden AB
Dec2020
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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=148075928&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 148075928 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 17453674 1BVL jtl: Acta Orthopaedica issn: 17453674 maglogo: N pubinfo: dt: Dec2020 vid: 91 iid: 6 pid: 59195 pub: Medical Journals Sweden AB artinfo: ui: 148075928 145554031 148075928 148075928 10.1080/17453674.2020.1803664 148075928 ppf: 699 ppct: 6 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Automated classification of hip fractures using deep convolutional neural networks with orthopedic surgeon-level accuracy: ensemble decision-making with antero-posterior and lateral radiographs. aug: au: Yamada, Yutoku Maki, Satoshi Kishida, Shunji Nagai, Haruki Arima, Junnosuke Yamakawa, Nanako Iijima, Yasushi Shiko, Yuki Kawasaki, Yohei Kotani, Toshiaki Shiga, Yasuhiro Inage, Kazuhide Orita, Sumihisa Eguchi, Yawara Takahashi, Hiroshi Yamashita, Takeshi Minami, Shohei Ohtori, Seiji affil: Department of Orthopaedic Surgery, Graduate School of Medicine, Chiba University, Japan sug: subj: Automation Hip Fractures Classification Neural Networks (Computer) Methods Deep Learning Methods Hip Radiography Human Comparative Studies Orthopedics Femur Neck Injuries Female Male Middle Age Aged Aged, 80 and Over Middle Aged: 45-64 years Aged: 65+ years Aged, 80 & over Female Male ab: Background and purpose — Deep-learning approaches based on convolutional neural networks (CNNs) are gaining interest in the medical imaging field. We evaluated the diagnostic performance of a CNN to discriminate femoral neck fractures, trochanteric fractures, and non-fracture using antero-posterior (AP) and lateral hip radiographs. Patients and methods — 1,703 plain hip AP radiographs and 1,220 plain hip lateral radiographs were included in the total dataset. 150 images each of the AP and lateral views were separated out and the remainder of the dataset was used for training. The CNN made the diagnosis based on: (1) AP radiographs alone, (2) lateral radiographs alone, or (3) both AP and lateral radiographs combined. The diagnostic performance of the CNN was measured by the accuracy, recall, precision, and F1 score. We further compared the CNN's performance with that of orthopedic surgeons. Results — The average accuracy, recall, precision, and F1 score of the CNN based on both anteroposterior and lateral radiographs were 0.98, 0.98, 0.98, and 0.98, respectively. The accuracy of the CNN was comparable to, or statistically significantly better than, that of the orthopedic surgeons regardless of radiographic view used. In the CNN model, the accuracy of the diagnosis based on both views was significantly better than the lateral view alone and tended to be better than the AP view alone. Interpretation — The CNN exhibited comparable or superior performance to that of orthopedic surgeons to discriminate femoral neck fractures, trochanteric fractures, and non-fracture using both AP and lateral hip radiographs. pubtype: Academic Journal doctype: diagnostic images research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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