Detection of incomplete atypical femoral fracture on anteroposterior radiographs via explainable artificial intelligence

Abstract

One of the key aspects of the diagnosis and treatment of atypical femoral fractures is the early detection of incomplete fractures and the prevention of their progression to complete fractures. However, an incomplete atypical femoral fracture can be misdiagnosed as a normal lesion by both primary care physicians and orthopedic surgeons; expert consultation is needed for accurate diagnosis. To overcome this limitation, we developed a transfer learning-based ensemble model to detect and localize fractures. A total of 1050 radiographs, including 100 incomplete fractures, were preprocessed by applying a Sobel filter. Six models (EfficientNet B5, B6, B7, DenseNet 121, MobileNet V1, and V2) were selected for transfer learning. We then composed two ensemble models; the first was based on the three models having the highest accuracy, and the second was based on the five models having the highest accuracy. The area under the curve (AUC) of the case that used the three most accurate models was the highest at 0.998. This study demonstrates that an ensemble of transfer-learning-based models can accurately classify and detect fractures, even in an imbalanced dataset. This artificial intelligence (AI)-assisted diagnostic application could support decision-making and reduce the workload of clinicians with its high speed and accuracy.

Proximal femur fracture detection on plain radiography via feature pyramid networks

“…EfficientNet was proposed to improve the efficiency of well-established convolutional neural networks by increasing architecture depth, resolution scaling and number of channels in intermediate layers to extract more fine-grained features 81 . It has been used by multiple related works 20 , 82 ; we implemented the EfficientNet-B5 version following recent works 82 . Both networks were initialized with weights pre-trained over the benchmark image classification dataset ImageNet 83 and implemented with the same preprocessing and inference procedures described in Sections " Data preprocessing " and " Inference and evaluation metrics ".…”

A Review of the Application of Artificial Intelligence in Orthopedic Diseases

“…However, general practitioners often miss the diagnosis of incomplete fractures. Taekyeong et al [63] developed an integrated migration learning-based model for detecting and localizing fractures to address this issue. They selected six models (EfficientNet B5, B6, B7, DenseNet 121, MobileNet V1, and V2) for migration learning to avoid the problem of needing more features due to small datasets, which in turn may lead to situations such as overfitting.…”