A new study by a team of researchers at Swansea University has developed a type of artificial intelligence that can help to detect and diagnose inflammatory arthritis in the early stages, resulting in better patient outcomes.
Ankylosing Spondylitis (AS) inflammatory arthritis is a long term condition which mostly affects the spine. The condition can cause back pain and stiffness, pain and swelling in the joints such as knees and hips, and fatigue. It usually develops gradually and worsens over time.
Eventually, the inflammation can cause severe damage to the spine, and can even cause the individual bones to fuse together, which is known as ankylosis. The exact cause of the condition is unknown, although researchers believe that there is a genetic link. It is more common in men than women, and tends to develop in the twenties and thirties.
The pioneering new study uses machine learning to develop a profile of the characteristics of people likely to be diagnosed with the condition. This data is then used to create links with anonymised health data to provide comparative analysis. The test data was found to have a prediction rate that was 70% to 80% accurate.
Dr Jonathan Kennedy, Data Lab Manager at NCPHWR and study lead: “Our study indicates the enormous potential machine learning has to help identify people with AS and better understand their diagnostic journeys through the health system.”
He added: “Early detection and diagnosis are crucial to secure the best outcomes for patients. Machine learning can help with this. In addition, it can empower GPs – helping them detect and refer patients more effectively and efficiently.”
“However, machine learning is in the early stages of implementation. To develop this, we need more detailed data to improve prediction and clinical utility.”
Professor Ernest Choy, Researcher at NCPHWR and Head of Rheumatology and Translational Research at Cardiff University, added: “On average, it takes eight years for patients with AS from having symptoms to receiving a diagnosis and getting treatment. Machine learning may provide a useful tool to reduce this delay.”
Professor Kieran Walshe, Director of Health and Care Research Wales, added: “It’s fantastic to see the cutting-edge role that machine learning can play in the early identification of patients with health conditions such as AS and the work being undertaken at the National Centre for Population Health and Wellbeing Research.”
He added: “Though it is in its early stages, machine learning clearly has the potential to transform the way that researchers and clinicians approach the diagnostic journey, bringing about benefits to patients and their future health outcomes.”
AS is the second most common form of inflammatory arthritis. It can eventually lead to restricted mobility of varying degrees of severity, although about 80% of patients are able to retain a degree of independent living.
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