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New imaging methods could help identify early stages of arthrosis

Arthrosis, a degenerative disease that affects the joints, becomes more common as people become older. The disease is becoming increasingly common among older people in Finland as well. Arthrosis is currently the subject of research in a number of projects funded by the Academy of Finland.

Obesity, joint injuries and heavy physical work are recognised risk factors for arthrosis. According to Adjunct Professor Jari Arokoski from the University of Eastern Finland, the root cause of arthrosis is still a mystery. The typical symptoms of arthrosis include deterioration and loss of cartilage from surfaces of the joints, which can be seen in X-rays as a narrowing of the joint space.

“It’s a condition that usually develops fairly slowly, over a long period of time. Once the cartilage tissue has been damaged, it’ll never heal completely. Some patients may experience severe joint pain and find that the condition restricts their functional capacity. A person’s X-rays may give indication of changes suggesting arthrosis, even without any experience of pain,” explains Arokoski.

In Finland, arthrosis results in 600,000 physician visits each year. The most expensive consequence of arthrosis is impaired functional capacity. According to statistics compiled by the Finnish Centre for Pensions and the Social Insurance Institution of Finland, nearly 10 per cent of disability pensions are directly attributable to arthrosis. The total annual costs due to musculoskeletal disorders amount to some 600 million euros, which is about 11 per cent of total medical expenses in Finland.

New imaging methods help identify tissue changes

The methods currently in use in primary healthcare to diagnose arthrosis are based on both clinical studies and radiography, says Academy Research Fellow and Associate Professor Simo Saarakkala from the University of Oulu.

“X-rays don’t show early stages of tissue changes – they can only be used to identify clear cases of tissue changes and cartilage damage. With increased imaging diagnostics accuracy, we could develop targeted treatment methods and improved prediction of disease progression,” explains Saarakkala.

Saarakkala heads a team that is trying to develop a computer-assisted method to analyse X-ray images. Such a method would increase the probability of identifying tissue changes and improve the uniformity of X-ray interpretation. The team has received funding from the Academy of Finland and the European Research Council (ERC). The team’s results show that X-rays are particularly effective in analysing bone tissue structure and how the structure changes at different disease stages. The team was also the first to show that computerised analysis of X-ray images can provide a three-dimensional image of real bone structure.

Ultrasound imaging, says Saarakkala, is also a feasible method for the early detection of arthrosis. It is a fairly inexpensive and simple method that has long been utilised in imaging the joints of people with rheumatic diseases.

“Ultrasonography has rarely been used for arthrosis. It’s long been a common misconception that ultrasound is unsuitable as a diagnostic method for arthrosis because it doesn’t provide an image of the load-bearing joint surface,” says Saarakkala.

Findings from a study by Saarakkala’s team, published in Scientific Reports earlier this year, completely dispel this misconception. The team was able to show that ultrasound imaging trumps X-ray in terms of detecting bone spurs, meniscal dislocation and joint cartilage changes in the load-bearing area of the knee. Previous research by Saarakkala’s team has shown that the computerised analysis method can also be put to good use in assessing subchondral bone density from ultrasound images. “Based on our studies, it’s highly recommendable to increase the use of ultrasonography in primary healthcare”, says Saarakkala.

In addition to clinical imaging, Saarakkala’s team also studies arthrosis-related tissue changes at microscopic resolution. The research is carried out by utilising tissue samples from organ donors and endoprosthesis patients. The team’s long-term vision is to combine data amassed via new imaging techniques with a computer-based statistical prediction model in order to help physicians in both primary and specialised healthcare make better treatment decisions.


Suomen Akatemia (Academy of Finland)