Performance of a new integrated computer-assisted system (CADe/CADx) for detection and characterization of colorectal neoplasia

Background Use of artificial intelligence may increase detection of colorectal neoplasia at colonoscopy by improving lesion recognition (CADe) and reduce pathology costs by improving optical diagnosis (CADx). Methods A multicenter library of >= 200000 images from 1572 polyps was used to train a combined CADe/CADx system. System testing was performed on two independent image sets (CADe: 446 with polyps, 234 without; CADx: 267) from 234 polyps, which were also evaluated by six endoscopists (three experts, three non-experts). Results CADe showed sensitivity, specificity, and accuracy of 92.9%, 90.6%, and 91.7%, respectively. Experts showed significantly higher accuracy and specificity, and similar sensitivity, while non-experts+CADe showed comparable sensitivity but lower specificity and accuracy than CADe and experts. CADx showed sensitivity, specificity, and accuracy of 85.0%, 79.4%, and 83.6%, respectively. Experts showed comparable performance, whereas non-experts+CADx showed comparable accuracy but lower specificity than CADx and experts. Conclusions The high accuracy shown by CADe and CADx was similar to that of experts, supporting further evaluation in a clinical setting. When using CAD, non-experts achieved a similar performance to experts, with suboptimal specificity.

Automated summary

The high accuracy demonstrated by CADe and CADx was similar to that of experts, suggesting the need for further evaluation in a clinical setting. When utilizing CAD, non-experts achieved a performance similar to experts, but with suboptimal specificity.