Fully automated endoscopic disease activity assessment in ulcerative colitis.
Gastrointest Endosc
; 93(3): 728-736.e1, 2021 03.
Article
en En
| MEDLINE
| ID: mdl-32810479
ABSTRACT
BACKGROUND AND AIMS:
Endoscopy is essential for disease assessment in ulcerative colitis (UC), but subjectivity threatens accuracy and precision. We aimed to pilot a fully automated video analysis system for grading endoscopic disease in UC.METHODS:
A developmental set of high-resolution UC endoscopic videos were assigned Mayo endoscopic scores (MESs) provided by 2 experienced reviewers. Video still-image stacks were annotated for image quality (informativeness) and MES. Models to predict still-image informativeness and disease severity were trained using convolutional neural networks. A template-matching grid search was used to estimate whole-video MESs provided by human reviewers using predicted still-image MES proportions. The automated whole-video MES workflow was tested using unaltered endoscopic videos from a multicenter UC clinical trial.RESULTS:
The developmental high-resolution and testing multicenter clinical trial sets contained 51 and 264 videos, respectively. The still-image informative classifier had excellent performance with a sensitivity of 0.902 and specificity of 0.870. In high-resolution videos, fully automated methods correctly predicted MESs in 78% (41 of 50, κ = 0.84) of videos. In external clinical trial videos, reviewers agreed on MESs in 82.8% (140 of 169) of videos (κ = 0.78). Automated and central reviewer scoring agreement occurred in 57.1% of videos (κ = 0.59), but improved to 69.5% (107 of 169) when accounting for reviewer disagreement. Automated MES grading of clinical trial videos (often low resolution) correctly distinguished remission (MES 0,1) versus active disease (MES 2,3) in 83.7% (221 of 264) of videos.CONCLUSIONS:
These early results support the potential for artificial intelligence to provide endoscopic disease grading in UC that approximates the scoring of experienced reviewers.
Texto completo:
1
Banco de datos:
MEDLINE
Asunto principal:
Colitis Ulcerosa
Tipo de estudio:
Clinical_trials
/
Prognostic_studies
Límite:
Humans
Idioma:
En
Revista:
Gastrointest Endosc
Año:
2021
Tipo del documento:
Article
País de afiliación:
Estados Unidos