Artificial intelligence for the classification of fractures around the knee in adults according to the 2018 AO/OTA classification system

Abstract

Background 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. Methods 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. Results 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. Conclusion 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.

Automating classification of osteoarthritis according to Kellgren-Lawrence in the knee using deep learning in an unfiltered adult population

“…This study is part of a model developed and validated as a diagnostic tool using our database containing radiographic examinations collected from our radiology department at Danderyd University Hospital. Recently, we have also published our results by Lind, et al [ 18 ] based on this diagnostic method as it was able to use artificial intelligence to identify and classify fractures. The details of the source of data, extracting methods, neural network setup, outcome measures, and statistical analysis were identical with the previously published article [ 18 ].…”

“…Recently, we have also published our results by Lind, et al [ 18 ] based on this diagnostic method as it was able to use artificial intelligence to identify and classify fractures. The details of the source of data, extracting methods, neural network setup, outcome measures, and statistical analysis were identical with the previously published article [ 18 ].…”

“…A supervised learning method was used to train a convolutional neural network of ResNet architecture [ 18 – 20 ]. We used this architecture because of its simplicity and lightweight with a total of 35 layers and a batch normalization for each convolutional layer as well as adaptive max pool (see Table 1 ).…”

Deep learning approach for guiding three‐dimensional computed tomography reconstruction of lower limbs for robotically‐assisted total knee arthroplasty

“…Moreover, Karnuta et al 18 used AI to differentiate between knee arthroplasty implants from different manufacturers with near-perfect accuracy. Lind et al 21 also showed that AI could identify fractures around the knee in adults according to the 2018 AO/OTA classification system with performance similar to that of senior orthopaedic surgeons. In this study, we used AI to perform 3D reconstruction of CT data of lower limbs and compared its performance with the operator-based approach.…”

“…[18][19][20] For example, Karnuta et al 18 used AI to identify arthroplasty implants from radiographs of the knee. Lind et al 21 also utilised AI to identify and classify fractures of the knee. In this study, we explored the use of AI to perform 3D reconstruction of CT data of lower limbs.…”