C2D2 is a machine learning algorithm that performs real-time 3D reconstruction of the colon during colonoscopies to detect deficient coverage.
The Colonoscopy Coverage Deficiency via Depth (C2D2) algorithm is a machine learning-based approach developed by Google Health and Google Research to improve colonoscopy coverage. It works by performing a local 3D reconstruction of the colon as images are captured during the procedure. Based on this reconstruction, C2D2 identifies areas of the colon that have been adequately covered and those that have remained outside the field of view. C2D2 provides real-time indications of deficient coverage, allowing endoscopists to immediately return to review missed areas. This functionality aims to increase the adenoma detection rate (ADR) and decrease the rate of interval colorectal cancers (CRCs). The algorithm computes depth maps for each frame of the colonoscopy video using a calibration-free, unsupervised learning method, and then calculates segment-by-segment coverage based on these maps. It has shown higher accuracy in detecting coverage deficiencies compared to physicians on synthetic videos and achieved 93% expert verification on real videos.
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