Artificial intelligence for the classification of fractures around the knee in adults according to the 2018 AO/OTA classification system.
<h4>Background</h4>Fractures around the knee joint are inherently complex in terms of treatment; complication rates are high, and they are difficult to diagnose on a plain radiograph. An automated way of classifying radiographic images could improve diagnostic accuracy and would enable p...
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doaj-82b04b59958b4b9ea32a13da49efe8f92021-04-11T04:30:36ZengPublic Library of Science (PLoS)PLoS ONE1932-62032021-01-01164e024880910.1371/journal.pone.0248809Artificial intelligence for the classification of fractures around the knee in adults according to the 2018 AO/OTA classification system.Anna LindEhsan AkbarianSimon OlssonHans NåsellOlof SköldenbergAli Sharif RazavianMax Gordon<h4>Background</h4>Fractures around the knee joint are inherently complex in terms of treatment; complication rates are high, and they are difficult to diagnose on a plain radiograph. An automated way of classifying radiographic images could improve diagnostic accuracy and would enable production of uniformly classified records of fractures to be used in researching treatment strategies for different fracture types. Recently deep learning, a form of artificial intelligence (AI), has shown promising results for interpreting radiographs. In this study, we aim to evaluate how well an AI can classify knee fractures according to the detailed 2018 AO-OTA fracture classification system.<h4>Methods</h4>We selected 6003 radiograph exams taken at Danderyd University Hospital between the years 2002-2016, and manually categorized them according to the AO/OTA classification system and by custom classifiers. We then trained a ResNet-based neural network on this data. We evaluated the performance against a test set of 600 exams. Two senior orthopedic surgeons had reviewed these exams independently where we settled exams with disagreement through a consensus session.<h4>Results</h4>We captured a total of 49 nested fracture classes. Weighted mean AUC was 0.87 for proximal tibia fractures, 0.89 for patella fractures and 0.89 for distal femur fractures. Almost ¾ of AUC estimates were above 0.8, out of which more than half reached an AUC of 0.9 or above indicating excellent performance.<h4>Conclusion</h4>Our study shows that neural networks can be used not only for fracture identification but also for more detailed classification of fractures around the knee joint.https://doi.org/10.1371/journal.pone.0248809 |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Anna Lind Ehsan Akbarian Simon Olsson Hans Nåsell Olof Sköldenberg Ali Sharif Razavian Max Gordon |
spellingShingle |
Anna Lind Ehsan Akbarian Simon Olsson Hans Nåsell Olof Sköldenberg Ali Sharif Razavian Max Gordon Artificial intelligence for the classification of fractures around the knee in adults according to the 2018 AO/OTA classification system. PLoS ONE |
author_facet |
Anna Lind Ehsan Akbarian Simon Olsson Hans Nåsell Olof Sköldenberg Ali Sharif Razavian Max Gordon |
author_sort |
Anna Lind |
title |
Artificial intelligence for the classification of fractures around the knee in adults according to the 2018 AO/OTA classification system. |
title_short |
Artificial intelligence for the classification of fractures around the knee in adults according to the 2018 AO/OTA classification system. |
title_full |
Artificial intelligence for the classification of fractures around the knee in adults according to the 2018 AO/OTA classification system. |
title_fullStr |
Artificial intelligence for the classification of fractures around the knee in adults according to the 2018 AO/OTA classification system. |
title_full_unstemmed |
Artificial intelligence for the classification of fractures around the knee in adults according to the 2018 AO/OTA classification system. |
title_sort |
artificial intelligence for the classification of fractures around the knee in adults according to the 2018 ao/ota classification system. |
publisher |
Public Library of Science (PLoS) |
series |
PLoS ONE |
issn |
1932-6203 |
publishDate |
2021-01-01 |
description |
<h4>Background</h4>Fractures around the knee joint are inherently complex in terms of treatment; complication rates are high, and they are difficult to diagnose on a plain radiograph. An automated way of classifying radiographic images could improve diagnostic accuracy and would enable production of uniformly classified records of fractures to be used in researching treatment strategies for different fracture types. Recently deep learning, a form of artificial intelligence (AI), has shown promising results for interpreting radiographs. In this study, we aim to evaluate how well an AI can classify knee fractures according to the detailed 2018 AO-OTA fracture classification system.<h4>Methods</h4>We selected 6003 radiograph exams taken at Danderyd University Hospital between the years 2002-2016, and manually categorized them according to the AO/OTA classification system and by custom classifiers. We then trained a ResNet-based neural network on this data. We evaluated the performance against a test set of 600 exams. Two senior orthopedic surgeons had reviewed these exams independently where we settled exams with disagreement through a consensus session.<h4>Results</h4>We captured a total of 49 nested fracture classes. Weighted mean AUC was 0.87 for proximal tibia fractures, 0.89 for patella fractures and 0.89 for distal femur fractures. Almost ¾ of AUC estimates were above 0.8, out of which more than half reached an AUC of 0.9 or above indicating excellent performance.<h4>Conclusion</h4>Our study shows that neural networks can be used not only for fracture identification but also for more detailed classification of fractures around the knee joint. |
url |
https://doi.org/10.1371/journal.pone.0248809 |
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