Core Concepts
A deep learning-based approach to accurately estimate the femur caput-collum-diaphyseal (CCD) angle from X-ray images, which is crucial for diagnosing and managing hip problems.
Abstract
This paper presents a deep learning-based method to estimate the femur caput-collum-diaphyseal (CCD) angle from X-ray images. The CCD angle is an important measurement used in the diagnosis and treatment of hip problems, but manual measurement can be time-consuming and prone to inter-observer variability.
The key highlights of the proposed approach are:
The method uses a U-Net architecture to learn features from X-ray images and predict the CCD angle. The U-Net is trained to predict heatmaps for the femur neck and shaft centerlines, which are then used to calculate the CCD angle.
The authors evaluated the method on a dataset of 201 hip X-ray images and achieved a mean absolute error of 4.3 degrees on the left femur and 4.9 degrees on the right femur, demonstrating high accuracy.
The authors also developed a prototype user interface that allows users to interact with the predictions, including the ability to edit the predicted lines and view the calculated CCD angle. The interface also supports voice control, which is important for the sterile operating room environment.
The user study conducted with the prototype showed high usability, with SUS scores between 80-90%, indicating the potential for the proposed method to be integrated into clinical workflows.
The results suggest that the deep learning-based approach has the potential to provide a more efficient and accurate technique for predicting the femur CCD angle, which could have significant implications for the diagnosis and management of hip problems.
Stats
The mean absolute error of the CCD angle prediction was 4.3 degrees on the left femur and 4.9 degrees on the right femur.
The mean centroids Euclidean distance and mean angular error for the individual femur centerlines were:
Left shaft centerline: 9.6 and 1.9 degrees
Left neck centerline: 14.0 and 2.7 degrees
Right neck centerline: 7.0 and 5.0 degrees
Right shaft centerline: 12.6 and 2.0 degrees
Quotes
"Our experimental results showed that the proposed method achieved good accuracy results, with an MAE of 4.3 degrees on left femur and 4.9 degrees on right femur."
"The proposed method has the potential to enhance patient outcomes by assisting in the faster, more accurate and efficient identification of hip disorders."
"Another advantage of the proposed approach is that it is simple to integrate into the clinical workflow with the voice command feature in our user interface providing ease of use in critical conditions of an interventional setting for hip fractures correction procedures, allowing for quick and precise calculation and visualizations of the CCD angle from X-ray images."