#176 System to detect vascular calcification through convolutional neural networks
Authors: Trujillo-Pulgar Gastón , Coronado-Daza Jorge Antonio 
Published: May 2024

Background and Aims

Cardiovascular disease is the major cause of death in patients with chronic kidney disease (CKD). It is increasingly believed that the presence of vascular calcification (VC) increases the risk of mortality due to cardiovascular events. VC is very common in CKD, especially in hemodialysis patient. To improve the life expectancy of patients, it is important to detect VC in the early stages of the disease. There are several ways to discover VC such as computed tomography (CT) scans, echocardiography, and X-Ray among others. However, one of the best options is the usage of X-ray images because is an affordable and simpler process in comparison to using CT scans or echocardiography. However, the problem with X-ray images is the difficulty of finding a well-trained professional that can detect the VC after analyzing the images which leads to possible misleading diagnoses. The goal of this study is to develop a prototype that detects Vascular Calcification from X-ray images in an automated way using the power of Convolutional Neural Networks (CNN).

Method

To train a CNN to detect VC from lumbosacral spine X-ray images, a set of 700 DICOM files complemented with 236 JPEG images was available. After getting the images, they had to be anonymized, and data, such as name and age was removed from each image. Then, the images were divided into three datasets: Train, Test, and Validation. Given that the dataset was not balanced between the pictures with VC (Positive) and those with no VC (Negative), it had to be reduced (Table 1).

The next step was implemented to clean the images and prepare them to feed the CNN. An example of preprocessing is that some X-ray images were of the entire body. For the analysis, the VC detection was assessed at the anterior and the posterior walls of the abdominal aorta adjacent to vertebrae L1-L4.

In addition to that, the DICOM images were converted to JPEG. After having the dataset prepared, a CNN architecture had to be selected. To achieve it, a revision of modern CNN architectures was performed. Two items were considered when selecting these: the performance on the ImageNet Large Scale Visual Recognition Challenge (ILSVRC) and if there was an available pre-trained model. With that criteria, pre-trained EfficientNet-B0 and ResNet50 models were used to build the prototype due to the limited dataset.

Results

After having the dataset prepared, the architectures selected, and the pre-trained models, the Training, Validation and Test stages were executed. For Training, with EfficientNet-B0, the Accuracy was 86.9%, while for Validation, the Accuracy was 57.4% (Fig. 1).

Using the RestNet50 model, the precision was 90.506% during Training, while for Validation, it was 54.8% (Fig. 2).

Finally, during the Testing stage, ResNet50 had a better performance, because EfficientNetB0 detected 80% for the negative class and 60% for the positive class.

After the CNN models were trained, an additional validation stage was performed to compare the model results with the analysis made by a radiologist and a nephrologist. This comparison determined that the model performed better with images having VC, while it was less accurate with images having no presence of VC. This contrast could have different reasons, one being possible false-positive images.

Conclusion

This study created a CNN model using pre-trained models on top of modern architectures such as EfficientNet-B0 and ResNet50. Tools were used to prepare the images as well as to train, validate and test the model. The Transfer Learning helped to build a model faster, giving high Accuracy rates with only a few images due to the limited dataset. However, the model could give better results if a wider and balanced set of images is available. Also, a better pre-processing stage can be executed to reduce false-positive images. For future work, this model can be used as the core of a system that not only accurately detects Vascular Calcification from X-Ray images, but also can determine the level of calcification using measures such as the Kauppila Index or Adragao.