Researchers from the University of Jyväskylä and the Central Finland Health Care District have developed an AI based neural network to detect an early knee osteoarthritis from x-ray images. AI was able to match a doctors’ diagnosis in 87% of cases. The result is important because x-rays are the primary diagnostic method for early knee osteoarthritis. An early diagnosis can save the patient from unnecessary examinations, treatments and even knee joint replacement surgery.
Osteoarthritis is the most common joint-related ailment globally. In Finland alone, it causes as many as 600,000 medical visits every year. It has been estimated to cost the national economy up to €1 billion every year.
The new AI based method was trained to detect a radiological feature predictive of osteoarthritis from x-rays. The finding is not at the moment included in the diagnostic criteria, but orthopaedic specialists consider it as an early sign of osteoarthritis. The method was developed in Digital Health Intelligence Lab at the University of Jyväskylä as a part of the AI Hub Central Finland project. It utilises neural network technologies that are widely used globally.
“The aim of the project was to train the AI to recognise an early feature of osteoarthritis from an x-ray. Something that experienced doctors can visually distinguish from the image, but cannot be done automatically,” explains Anri Patron, the researcher responsible for the development of the method.
In practice, the AI tries to detect whether there is spiking on the tibial tubercles in the knee joint or not. Tibial spiking can be a sign of osteoarthritis.
The reliability of the method was evaluated together with specialists from the Central Finland Healthcare District.
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