In a major leap for AI in healthcare, Alibaba has introduced a groundbreaking artificial intelligence model that could transform how Colorectal Cancer is detected. Developed by Alibaba DAMO Academy in collaboration with Guangdong Provincial People’s Hospital and other institutions, the new system, known as DAMO COCA, uses non-contrast CT scans to identify cancer with remarkable accuracy, without requiring bowel preparation.

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Traditional colorectal cancer screening methods, such as colonoscopy, are effective but often avoided due to their invasive nature and the need for bowel preparation. DAMO COCA can change this paradigm by enabling non-invasive cancer screening through routine CT scans.

This innovation makes early detection more accessible and patient-friendly, potentially increasing screening rates worldwide.

Damo Academy said its Coca AI model has higher sensitivity in detecting colorectal cancer than radiologists (Pic: Handout/SCMP)

According to findings published in Annals of Oncology, the AI model achieved a sensitivity of 86.6 per cent and an exceptional specificity of 99.8 per cent. Experts say this means the system is highly effective at correctly identifying both cancer cases and non-cancer cases, reducing the chances of false positives.

Even more notably, DAMO COCA demonstrated improved detection in areas where lesions are typically missed by conventional screening methods - highlighting its potential to catch cancers earlier and more reliably.

Last April, Damo’s Panda model received a “breakthrough device” designation from the US Food and Drug Administration, granting the platform an expedited review and approval process.

The study analysed more than 27,000 CT scans, making it one of the largest datasets used for colorectal cancer AI research. The model employs a two-stage deep learning framework designed to interpret the complex structure of the intestines more accurately.

This advanced architecture allows the AI to detect subtle abnormalities that may go unnoticed during manual review, offering a powerful tool for radiologists and healthcare providers.

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