AI will soon be able to prewarn of disease

Many serious diseases would be detected earlier if the health care had the technical means for examining X-ray images, can be read in an article at Chalmers University of Technology.

Chalmers University of Technology and Sahlgrenska University Hospital now work together to develop a method based on artificial intelligence to assess computed tomographic images (3D X-ray) of the heart’s coronary arteries. The tool is developed not least thanks to image data from a large Swedish population study. Health care has so far only just had a first taste of all the opportunities offered by artificial intelligence, AI. Sahlgrenska and Chalmers AI Research Center (Chair) recently launched a strategic research collaboration on AI in health care.

“AI is developing rapidly at the moment”, says Fredrik Kahl, professor of computer vision and image analysis at the department of Electrical Engineering at Chalmers. “There are many unexplored opportunities for AI in medical technology, for example to make early diagnoses and to support health care staff during surgery.”

Cardiovascular disease is still the most common cause of death in Sweden and the world. But conditions have never been better to identify individual risks for, for example, stroke, COPD, sudden cardiac arrest, myocardial infarction and other heart diseases. This is due to several advances.

In addition to AI technology itself becoming more and more advanced, new technology in the health care system makes it possible to take pictures of the heart, lungs and blood vessels in a way not previously possible. It is also possible to image and measure the distribution of fat in the body. In addition, there is now a sufficiently large image bank to use thanks to the population study Scapis. The study comprises 30,000 Swedes and is a collaboration between six universities and six university hospitals. Images and information collected by Scapis are now used in several medical research projects where computers will learn to interpret computed tomographic images of human organs.

“We are currently working with Sahlgrenska to develop an algorithm that can be used for segmentation and classification of three-dimensional computed tomographic images of the coronary arteries”, says Fredrik Kahl.

Jennifer Alvén is also involved in the project. She is a doctoral student in medical image analysis and in the process of developing an algorithm that allows the computer system to read the coronary arteries all by itself.

“It is great that the research is really taking off now”, says Jennifer Alvén. “I am training the computer system through deep learning so that it can recognize the coronary arteries of the heart and the areas where the vessels hold calcium and fat, which could lead to future heart problems.”

When the computer learns to locate the coronary arteries, it needs actual cases to compare with. In this case 600 X-ray images from the Scapis project, where radiologists have outlined the coronary arteries digitally. Each such image takes about half a working day for medical staff to assess. The computer will now be trained to do the same job as the medical doctors.

“The goal is to have the 600 images ready at the turn of the year. It will be the world’s largest data collection of coronary arteries images in a research context”, says Jennifer Alvén.

The AI assessment will be as accurate as the assessment made by humans but will go much faster once the computer is trained. Thus, analysing all coronary arteries images for the 30,000 people in the survey will no longer be an impossible task. In the next step, AI can help in discovering undetected connections and patterns, when a follow-up is done to find out which of the people in the study have really been affected by, for example, myocardial infarction and stroke.

Scapis data is also used in another project to study connections between the presence of fat within the pericardial sac and cardiovascular disease. The Chalmers researchers have developed a working algorithm for this, which has been passed on to health care software development specialists.

“We hope that the algorithm for coronary arteries also can be passed on for health care use”, says Jennifer Alvén. “It would be interesting to include it in one of the larger platforms already available for coronary arteries surgery.”

There are many needs and possible uses for image analysis in health care. A clear example of this is cancerous tumours of the kidneys, which are often detected at a much later stage than they could in fact have been spotted on X-rays.

“Early detection of cancerous tumours in the kidneys would benefit greatly by an automatic algorithm”, says Fredrik Kahl. “When studying computed tomography images of people later diagnosed with kidney cancer, it has been found that in about fifty percent of the cases, medical doctors would have been able to detect the tumour on the X-rays. The problem is that no one has been looking specifically for such tumours in these images. Here is a gap that AI could fill.”

Both researchers have experienced a positive attitude and great interest from medical staff at Sahlgrenska for new AI tools. The lead times, however, are always long before new methods can be introduced in health care.

A possible future scenario is that all X-ray images taken, for whatever reason, undergo an automatic AI examination to detect signs of the most serious diseases as early as possible. This would mean a huge opportunity to reduce patients’ suffering and improve public health.

Facts about the population study Scapis

Scapis is a Swedish population study that examines the cardiovascular status of 30,000 randomly selected women and men aged 50–64 years. The recruitment phase has been completed and analysis of collected data is now underway.
The purpose is to be able to identify individual risks such as stroke, COPD, sudden cardiac arrest, myocardial infarction and other heart diseases.
The goal is to gain greater knowledge about the origin of the diseases in order to prevent them before they occur.
Six universities and six university hospitals in collaboration lead and run Scapis.
Scapis is funded by the Swedish Heart-Lung Foundation as the main financier and with significant contributions from the Knut and Alice Wallenberg Foundation, Vinnova, the Swedish Research Council and the university hospitals and the universities themselves.

Text: Yvonne Jonsson, Chalmers University of Technology
Photo: Henrik Sandsjö