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
| Autor: | Andreas Stark, Filip Emilson, Tone Antonsson, Jakub Olczak, Max Gordon, Ali Sharif Razavian |
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| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
Ankle Fractures
03 medical and health sciences Deep Learning 0302 clinical medicine medicine Humans Orthopedics and Sports Medicine 030212 general & internal medicine Malleolar fracture Sweden Orthodontics Orthopedic surgery 030222 orthopedics business.industry Articles General Medicine Radiography Orthopedic trauma medicine.anatomical_structure Fracture (geology) Surgery Ankle business Algorithms RD701-811 Research Article |
| Zdroj: | Acta Orthopaedica, Vol 92, Iss 1, Pp 102-108 (2020) Acta Orthopaedica article-version (VoR) Version of Record |
| ISSN: | 1745-3682 1745-3674 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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