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


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2022-08-16

AUTHORS

Masayoshi Yamada, Ryosaku Shino, Hiroko Kondo, Shigemi Yamada, Hiroyuki Takamaru, Taku Sakamoto, Pradeep Bhandari, Hitoshi Imaoka, Aya Kuchiba, Taro Shibata, Yutaka Saito, Ryuji Hamamoto

ABSTRACT

BackgroundImproved 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.MethodsWe 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.ResultsIn 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).ConclusionsThe developed AI system can help non-expert endoscopists make differential diagnoses of colorectal neoplasia on par with expert endoscopists during colonoscopy.(229/250 words). More... »

PAGES

879-889

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/s00535-022-01908-1

DOI

http://dx.doi.org/10.1007/s00535-022-01908-1

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1150276818

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/35972582


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