Insufficient bowel preparation accounts for up to 42% of missed adenomas in colonoscopy. However, major analysis programs found no correlation between adenoma detection rate and the human-rated Boston Bowel Preparation Scale (BBPS), indicating limitations of the scale. We therefore aimed to develop an Open-Source Automatic Bowel Preparation Scale (OSABPS) based on artificial intelligence that is correlated to the polyp detection rate.

The OSABPS was trained on 50,000 frames from 20 colonoscopies from 3 hospitals. It involved quantifying the presence of fecal matter within the colonoscopy frames, using an approach termed the fecal ratio—the proportion of pixels identified as feces (F) relative to those identified as mucosal tissue (M) (OSABPS = F/M)—thereby making 0 the optimal score, indicating a perfect cleansing. The Youden J was used to set the threshold because it determines the optimal balance between sensitivity and specificity. The algorithm was then tested on 1405 colonoscopies from 3 hospitals (internal validation) and 5525 frames from a public colonoscopy database (Nerthus, external validation).

Internal validation: the OSABPS correlated significantly with the BBPS (Pearson r = –0.42, P < .001). A threshold of 0.09 OSABPS was determined using the Youden J. The polyp detection rate was higher for colonoscopies below the threshold of the Youden J (2-proportion z-test, P < .001). External validation: the OSABPS correlated significantly with the BBPS (Pearson r = –0.70, P < .001).

The OSABPS can automatically, instantly, and without human bias assess bowel preparation quality. Colonoscopies with an OSABPS of >0.09 should be considered for reexamination. The OSABPS’s open-source nature allows free implementation.