Transformation of Angiotomography Images through the use of Artificial Intelligence.

This archive includes automatic translations. Audio, video and text inside images remain in their original language.

Editorial note: this article documents an educational project and its results on the data used. It does not establish clinical validation or authorisation to diagnose or treat patients. Proposed healthcare applications are retained as the team’s goals, not demonstrated capabilities.

Latam online. Second Edition. 2021

Contrast angiotomography What is the problem?

The challenge

The intravenous contrast media used in contrast-enhanced angiotomography can have an impact on the nephrological health of patients, especially in those who have a history of high blood pressure, hypercholesterolemia, genetic history, older adults (60 years and older) and an increased cardiovascular risk (1), that is, they are more prone to:

have strokes,
acute myocardial infarction,
peripheral arterial disease and,
diseases of the aorta in general

There is the possibility of performing simple angiotomography that does not require the use of contrast media and has a lower cost (around $200.00 difference), but these do not allow complete visualization of the aortic structures clearly (2), so they are not useful in many cases, such as that of Jesús.

Why did we choose Angiotomography?

The opportunity. How do we propose to solve it?

ANGIOP.AI (Angiotomography Image Transformation System) generates significant advances in the analysis of medical images through artificial intelligence analysis models for angiotomography, providing an alternative that means less impact on the health of patients who, due to their diagnosis and general condition, require this type of analysis to be performed on a recurring basis.

Figure 1. Structure of the GAN model applied to the transformation of Angiotomography images

ANGIOP.AI bases its operation on the training, validation and evaluation of a CycleGAN model to process non-contrast CT angiography images and transform them into images similar to those that would be obtained using contrast methods; A CycleGan is trained to read an image from dataset

What are the benefits?

ANGIOP.AI is aligned with the fulfillment of the United Nations sustainable development goals for 2030.

We explain it step by step: methodology, model used and datasets

The datasets used correspond to those provided by Dr. Gonzalo Pullas, director of the Medicine degree at the University of the Armed Forces; who provided the images of angiotomography with contrast and simple angiotomography (without contrast) of 10 anonymous patients.

The total number of images provided was 5,144, size 512 x 512, in gray scale, to which a data exploration was applied - statistical analysis to rule out color and 3D images, without atypical data and with differences in pixel distribution (zones / sections). It is important to mention that the angiotomograms were taken in the same anatomical area, but on different occasions, that is, the images do not correspond to 1:1 parity.

The model used is CycleGan with -1 to 1 scale image pre-processing for model input. The discriminators are convolutional networks with 5 layers that receive 256x256 images. The 4 layers are pattern definition and a classification layer. The generator uses three convolutional layers and six residual blocks. To calculate the cost functions, both for real and fake images, the mean squared error process is being used.

The model uses the following parameters:

20000 epochs
Learning rate of 0.0001

The following steps were applied for training:

Select a number of real images
The generator takes the real images and adds noise to them to create a number of fake images
Train the discriminator, making it classify the images as false or true a certain number of times or epochs
Generate another number of fake images to train the generator
The model is trained
To finalize the model, the veracity of the execution must be reviewed, reviewing the loss graph over time and reviewing the samples generated by the model.

Let's see the results:

Open video or resource

Lessons learned

Teach the model to validate the input images if they correspond to the angiothoracic area.
Increase the Dataset for future training.
Validation of the generated images with a panel of medical experts is required.

What's coming: Future of ANGIOP.AI

Use the data generated to measure the impact of the use of the system on reducing the incidence of kidney diseases.
Develop a Web application.

References

(1) Cueva Torres, Dr., F. (2021). Epidemiology and Management of Aortic Diseases — SIAC. Inter-American Society of Cardiology.

(2) RadiologyInfo for patients (2020). Contrast Materials.

(3) World Heart Federation (2016) World Congress of cardiology & Cardiovascular Health

(4) Ferreira, J (2017) News in contrast nephropathy. Pontifical Bolivarian University, Medellín—Colombia. ELSEVIER Volume 14 Number 2

Members

Diego Chiza, Ana Gayosso, Gabriela Jiménez, Paola Peralta, Patricia Roman José Daniel Sacoto, María Teresa Vergara, Hilario Villamar, David Medrano.

Project presentation: DemoDay

Open video or resource

Repository

The source code for this project can be found at: github

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