A recent study conducted by the University of Cambridge has revealed the ability of artificial intelligence (AI) to diagnose celiac disease (coeliac disease) with accuracy comparable to that of specialist doctors, paving the way for the use of this advanced technology to improve medical efficiency and reduce pressure on healthcare systems.
اضافة اعلان
The results of the study were published in The New England Journal of Medicine, where researchers developed a machine learning algorithm that was able to diagnose celiac disease with 97% accuracy, based on the analysis of small intestine biopsy images.
The algorithm was trained using over 3,400 scanned biopsies from four hospitals within the UK's National Health Service (NHS), ensuring its ability to operate in various medical environments.
Celiac disease is an autoimmune disorder triggered by the consumption of gluten, affecting one in every 100 people worldwide. Its symptoms vary, including abdominal pain, diarrhea, fatigue, and anemia, which makes diagnosis challenging and often delayed for years.
Typically, diagnosis relies on a biopsy of the duodenum to examine the condition of the villi in the small intestine, a procedure that requires high accuracy, and doctors may interpret the results differently, leading to varying diagnoses.
In this study, AI demonstrated significant capability in diagnosing the disease accurately, with a sensitivity of 95% (accurately diagnosing affected cases) and a specificity of 98% (accurately excluding non-affected cases), results comparable to those of experienced medical professionals.
The researchers noted that this technology could speed up diagnostic processes and reduce the burden on doctors by automating routine cases, allowing them to focus on more complex cases. It could also play a crucial role in improving diagnostic quality in countries with limited medical resources.
The study concluded that AI not only enhances diagnostic efficiency but could revolutionize how celiac disease is detected and treated, especially in regions with shortages of specialized medical personnel.
Agencies
