Calculation of Femur Caput Collum Diaphyseal angle for X-Rays images using Semantic Segmentation

TL;DR

This paper presents a deep learning method for accurately estimating the femur CCD angle from X-ray images, aiming to improve diagnosis and surgical planning for hip problems.

Contribution

The study introduces a novel deep learning algorithm and interactive prototype for automatic femur CCD angle measurement from X-rays, including voice command functionality for surgical settings.

Findings

Mean absolute error of 4.3 degrees for left femur

Mean absolute error of 4.9 degrees for right femur

High accuracy in predicting femur CCD angles

Abstract

This paper investigates the use of deep learning approaches to estimate the femur caput-collum-diaphyseal (CCD) angle from X-ray images. The CCD angle is an important measurement in the diagnosis of hip problems, and correct prediction can help in the planning of surgical procedures. Manual measurement of this angle, on the other hand, can be time-intensive and vulnerable to inter-observer variability. In this paper, we present a deep-learning algorithm that can reliably estimate the femur CCD angle from X-ray images. To train and test the performance of our model, we employed an X-ray image dataset with associated femur CCD angle measurements. Furthermore, we built a prototype to display the resulting predictions and to allow the user to interact with the predictions. As this is happening in a sterile setting during surgery, we expanded our interface to the possibility of being used only by voice commands. Our results show that our deep learning model predicts the femur CCD angle on X-ray images with great accuracy, with a mean absolute error of 4.3 degrees on the left femur and 4.9 degrees on the right femur on the test dataset. Our results suggest that deep learning has the potential to give a more efficient and accurate technique for predicting the femur CCD angle, which might have substantial therapeutic implications for the diagnosis and management of hip problems.