Improved Detection of Elusive Polyps via Machine Learning

Posted by Yossi Matias, Vice President and Ehud Rivlin, Research Scientist, Google Research

With the increasing ability to consistently and accurately process large amounts of data, particularly visual data, computer-aided diagnostic systems are more frequently being used to assist physicians in their work. This, in turn, can lead to meaningful improvements in health care. An example of where this could be especially useful is in the diagnosis and treatment of colorectal cancer (CRC), which is especially deadly and results in over 900K deaths per year, globally. CRC originates in small pre-cancerous lesions in the colon, called polyps, the identification and removal of which is very successful in preventing CRC-related deaths.

The standard procedure used by gastroenterologists (GIs) to detect and remove polyps is the colonoscopy, and about 19 million such procedures are performed annually in the US alone. During a colonoscopy, the gastroenterologist uses a camera-containing probe to check the intestine for pre-cancerous polyps and early signs of cancer, and removes tissue that looks worrisome. However, complicating factors, such as incomplete detection (in which the polyp appears within the field of view, but is missed by the GI, perhaps due to its size or shape) and incomplete exploration (in which the polyp does not appear in the camera’s field of view), can lead to a high fraction of missed polyps. In fact, studies suggest that 22%–28% of polyps are missed during colonoscopies, of which 20%–24% have the potential to become cancerous (adenomas).

Today, we are sharing progress made in using machine learning (ML) to help GIs fight colorectal cancer by making colonoscopies more effective. In “Detection of Elusive Polyps via a Large Scale AI System”, we present an ML model designed to combat the problem of incomplete detection by helping the GI detect polyps that are within the field of view. This work adds to our previously published work that maximizes the coverage

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