Performance of a new integrated computer-assisted system (CADe/CADx) for detection and characterization of colorectal neoplasia.
Endoscopy
; 54(2): 180-184, 2022 02.
Article
em En
| MEDLINE
| ID: mdl-33494106
ABSTRACT
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 ≥â200â 000 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 92.9â%, 90.6â%, and 91.7â%, respectively. Experts showed significantly higher accuracy and specificity, and similar sensitivity, while non-expertsâ+âCADe showed comparable sensitivity but lower specificity and accuracy than CADe and experts. CADx showed sensitivity, specificity, and accuracy of 85.0â%, 79.4â%, and 83.6â%, respectively. Experts showed comparable performance, whereas non-expertsâ+âCADx 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.
Texto completo:
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Base de dados:
MEDLINE
Assunto principal:
Neoplasias Colorretais
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Adenoma
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Pólipos do Colo
/
Juniperus
Tipo de estudo:
Clinical_trials
/
Diagnostic_studies
Limite:
Humans
Idioma:
En
Ano de publicação:
2022
Tipo de documento:
Article