Long Cylindrical Filling Defects in Bile Duct

Robust automated prediction of the revised Vienna Classification in colonoscopy using deep learning: development and initial external validation

BACKGROUND

Improved optical diagnostic technology is needed that can be used by also outside expert centers. Hence, we developed an artificial intelligence (AI) system that automatically and robustly predicts the pathological diagnosis based on the revised Vienna Classification using standard colonoscopy images.

METHODS

We prepared deep learning algorithms and colonoscopy images containing pathologically proven lesions (56,872 images, 6775 lesions). Four classifications were adopted: revised Vienna Classification category 1, 3, and 4/5 and normal images. The best algorithm-ResNet152-in the independent internal validation (14,048 images, 1718 lesions) was used for external validation (255 images, 128 lesions) based on neoplastic and non-neoplastic classification. Diagnostic performance of endoscopists was compared using a computer-assisted interpreting test.

RESULTS

In the internal validation, the sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and accuracy for adenoma (category 3) of 84.6% (95% CI 83.5-85.6%), 99.7% (99.5-99.8%), 90.8% (89.9-91.7%), 89.2% (88.5-99.0%), and 89.8% (89.3-90.4%), respectively. In the external validation, ResNet152's sensitivity, specificity, PPV, NPV, and accuracy for neoplastic lesions were 88.3% (82.6-94.1%), 90.3% (83.0-97.7%), 94.6% (90.5-98.8%), 80.0% (70.6-89.4%), and 89.0% (84.5-93.6%), respectively. This diagnostic performance was superior to that of expert endoscopists. Area under the receiver-operating characteristic curve was 0.903 (0.860-0.946).

CONCLUSIONS

The developed AI system can help non-expert endoscopists make differential diagnoses of colorectal neoplasia on par with expert endoscopists during colonoscopy. (229/250 words).