Ankle fracture classification using deep learning: automating detailed AO Foundation/Orthopedic Trauma Association (AO/OTA) 2018 malleolar fracture identification reaches a high degree of correct classification.
Background and purpose — Classification of ankle fractures is crucial for guiding treatment but advanced classifications such as the AO Foundation/Orthopedic Trauma Association (AO/OTA) are often too complex for human observers to learn and use. We have therefore investigated whether an automated al...
| Publicado en: | Acta Orthopaedica Vol. 92; no. 1; pp. 102 - 109 |
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| Autores principales: | , , , , , |
| Formato: | diagnostic images equations & formulas research tables/charts Journal Article |
| Publicado: |
Medical Journals Sweden AB
Feb2021
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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=148980653&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 148980653 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 17453674 1BVL jtl: Acta Orthopaedica issn: 17453674 maglogo: N pubinfo: dt: Feb2021 vid: 92 iid: 1 pid: 59195 pub: Medical Journals Sweden AB artinfo: ui: 148980653 146595562 148980653 148980653 10.1080/17453674.2020.1837420 148980653 ppf: 102 ppct: 7 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Ankle fracture classification using deep learning: automating detailed AO Foundation/Orthopedic Trauma Association (AO/OTA) 2018 malleolar fracture identification reaches a high degree of correct classification. aug: au: Olczak, Jakub Emilson, Filip Razavian, Ali Antonsson, Tone Stark, Andreas Gordon, Max affil: Karolinska Institute, Institution for Clinical Sciences, Danderyd University Hospital, Stockholm, Sweden sug: subj: Ankle Fractures Classification Deep Learning Automation Foundations Orthopedics Organizations Orthopedic Care Standards Human Neural Networks (Computer) Computers and Computerization Ankle Radiography Descriptive Statistics Confidence Intervals Odds Ratio ab: Background and purpose — Classification of ankle fractures is crucial for guiding treatment but advanced classifications such as the AO Foundation/Orthopedic Trauma Association (AO/OTA) are often too complex for human observers to learn and use. We have therefore investigated whether an automated algorithm that uses deep learning can learn to classify radiographs according to the new AO/OTA 2018 standards. Method — We trained a neural network based on the ResNet architecture on 4,941 radiographic ankle examinations. All images were classified according to the AO/OTA 2018 classification. A senior orthopedic surgeon (MG) then re-evaluated all images with fractures. We evaluated the network against a test set of 400 patients reviewed by 2 expert observers (MG, AS) independently. Results — In the training dataset, about half of the examinations contained fractures. The majority of the fractures were malleolar, of which the type B injuries represented almost 60% of the cases. Average area under the area under the receiver operating characteristic curve (AUC) was 0.90 (95% CI 0.82–0.94) for correctly classifying AO/OTA class where the most common major fractures, the malleolar type B fractures, reached an AUC of 0.93 (CI 0.90–0.95). The poorest performing type was malleolar A fractures, which included avulsions of the fibular tip. Interpretation — We found that a neural network could attain the required performance to aid with a detailed ankle fracture classification. This approach could be scaled up to other body parts. As the type of fracture is an important part of orthopedic decision-making, this is an important step toward computer-assisted decision-making. 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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