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Endoscopy OCT Combined with AI Diagnoses Colon Cancer with High Accuracy

By HospiMedica International staff writers
Posted on 02 Aug 2022
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Image: The results were published in the June issue of the Journal of Biophotonics (Photo courtesy of Washington University in St. Louis)
Image: The results were published in the June issue of the Journal of Biophotonics (Photo courtesy of Washington University in St. Louis)

Screening for colon cancer now relies on human visual inspection of tissue during a colonoscopy procedure. This technique, however, does not detect and diagnose subsurface lesions. Now, a research team has combined optical coherence tomography (OCT) and machine learning to develop a colorectal cancer imaging tool that may one day improve the traditional endoscopy currently used by doctors.

An endoscopy OCT essentially shines a light in the colon to help a clinician see deeper to visualize and diagnose abnormalities. Biomedical engineers at Washington University in St. Louis (St. Louis, MO, USA) have developed a small OCT catheter, which uses a longer wavelength of light, to penetrate 1-2 mm into the tissue samples. The technique provided more information about an abnormality than surface-level, white-light images currently used by physicians. The researchers used the imaging data to train a machine learning algorithm to differentiate between “normal” and “cancerous” tissue. The combined system allowed them to detect and classify cancerous tissue samples with a 93% diagnostic accuracy.

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Washington University in St. Louis 


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