Artificial Intelligence Improves the Accuracy of Residents in the Diagnosis of Hip Fractures –A Multicenter Study
Background
Less experienced clinicians sometimes make misdiagnosis of hip fractures. We developed computer-aided diagnosis (CAD) system for hip fractures on plain X-rays using a deep learning model trained on a large dataset. In this study, we examined whether the accuracy of the diagnosis of hip fracture of the residents could be improved by using this system.
Methods
A deep convolutional neural network approach was used for machine learning. Pytorch 1.3 and Fast.ai 1.0 were applied as frameworks, and an EfficientNet-B4 model (a pre-trained ImageNet model) was used. We handled the 5295 X-rays from the patients with femoral neck fracture or femoral trochanteric fracture from 2009 to 2019. We excluded cases in which the bilateral hips were not included within an image range, and cases of femoral shaft fracture and periprosthetic fracture. Finally, we included 5242 AP pelvic X-rays from 4,851 cases. These images were divided into 5242 images that included the fracture site and 5242 images that did not. Thus, a total of 10484 images were used for machine learning. The accuracy, sensitivity, specificity, F-value, and area under the curve (AUC) were assessed. Gradient-weighted class activation mapping (Grad-CAM) was used to conceptualize the basis for the diagnosis of the fracture by the deep learning algorithm. Secondly, we conducted a controlled experiment with clinicians. Thirty-one residents and four orthopedic surgery fellows were tested using 300 hip fracture images that were randomly extracted from the dataset. We evaluated the diagnostic accuracy with and without the use of the CAD system for each of the 300 images.
Results
The accuracy, sensitivity, specificity, F-value, and AUC were 96.1%, 95.2%, 96.9%, 0.961, and 0.99, respectively, with the correct diagnostic basis generated by Grad-CAM. In the controlled experiment, the diagnostic accuracy of the residents significantly improved when they used the CAD system.
Conclusions
We developed a newly CAD system with a deep learning algorithm from a relatively large dataset from multiple institutions. Our system achieved high diagnostic performance. Our system improved the diagnostic accuracy of residents for hip fractures.
Level of Evidence
Level Ⅲ, Foundational evidence, before-after study. Clinical relevance: high
Figure 1
Figure 2
Figure 3
Figure 4
This is a list of supplementary files associated with this preprint. Click to download.
Posted 25 Sep, 2020
On 21 Nov, 2020
Received 12 Nov, 2020
Received 22 Oct, 2020
On 21 Oct, 2020
Invitations sent on 01 Oct, 2020
On 01 Oct, 2020
On 14 Sep, 2020
On 13 Sep, 2020
On 13 Sep, 2020
On 11 Sep, 2020
Artificial Intelligence Improves the Accuracy of Residents in the Diagnosis of Hip Fractures –A Multicenter Study
Posted 25 Sep, 2020
On 21 Nov, 2020
Received 12 Nov, 2020
Received 22 Oct, 2020
On 21 Oct, 2020
Invitations sent on 01 Oct, 2020
On 01 Oct, 2020
On 14 Sep, 2020
On 13 Sep, 2020
On 13 Sep, 2020
On 11 Sep, 2020
Background
Less experienced clinicians sometimes make misdiagnosis of hip fractures. We developed computer-aided diagnosis (CAD) system for hip fractures on plain X-rays using a deep learning model trained on a large dataset. In this study, we examined whether the accuracy of the diagnosis of hip fracture of the residents could be improved by using this system.
Methods
A deep convolutional neural network approach was used for machine learning. Pytorch 1.3 and Fast.ai 1.0 were applied as frameworks, and an EfficientNet-B4 model (a pre-trained ImageNet model) was used. We handled the 5295 X-rays from the patients with femoral neck fracture or femoral trochanteric fracture from 2009 to 2019. We excluded cases in which the bilateral hips were not included within an image range, and cases of femoral shaft fracture and periprosthetic fracture. Finally, we included 5242 AP pelvic X-rays from 4,851 cases. These images were divided into 5242 images that included the fracture site and 5242 images that did not. Thus, a total of 10484 images were used for machine learning. The accuracy, sensitivity, specificity, F-value, and area under the curve (AUC) were assessed. Gradient-weighted class activation mapping (Grad-CAM) was used to conceptualize the basis for the diagnosis of the fracture by the deep learning algorithm. Secondly, we conducted a controlled experiment with clinicians. Thirty-one residents and four orthopedic surgery fellows were tested using 300 hip fracture images that were randomly extracted from the dataset. We evaluated the diagnostic accuracy with and without the use of the CAD system for each of the 300 images.
Results
The accuracy, sensitivity, specificity, F-value, and AUC were 96.1%, 95.2%, 96.9%, 0.961, and 0.99, respectively, with the correct diagnostic basis generated by Grad-CAM. In the controlled experiment, the diagnostic accuracy of the residents significantly improved when they used the CAD system.
Conclusions
We developed a newly CAD system with a deep learning algorithm from a relatively large dataset from multiple institutions. Our system achieved high diagnostic performance. Our system improved the diagnostic accuracy of residents for hip fractures.
Level of Evidence
Level Ⅲ, Foundational evidence, before-after study. Clinical relevance: high
Figure 1
Figure 2
Figure 3
Figure 4