They said the cancer type is most treatable if it is detected before they have spread to other sites in the body.
Colonoscopy is regarded as the "gold standard" for diagnosis, but the scientists said the procedure is uncomfortable and invasive and can lead to complications, leaving patients less willing to undergo screening.
The scientists leveraged AI techniques, where systems self-learned to perform specific tasks without explicit instructions, to develop a less invasive diagnostic method that can detect bowel cancer in at-risk patients.
According to the study, the technology works by screening for methylation markers, which are chemical modifications to DNA which are frequently found in tumours.
The researchers first created a diagnostic model based on nine methylation markers associated with bowel cancer, which they identified by studying samples of the blood component plasma from 801 patients with the malignant disease, as well as 1,021 controls.
This model accurately distinguished patients from healthy individuals with a sensitivity of 87.5 per cent, and outperformed a clinically available blood test named CEA.
With longer follow-up periods, the researchers said the machine learning model needs to be further assessed for reliability in detecting tumour in patients.
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