Artificial Intelligence and Deep Learning for Upper Gastrointestinal Neoplasia.
Gastroenterology
; 162(4): 1056-1066, 2022 04.
Article
em En
| MEDLINE
| ID: mdl-34902362
ABSTRACT
Upper gastrointestinal (GI) neoplasia account for 35% of GI cancers and 1.5 million cancer-related deaths every year. Despite its efficacy in preventing cancer mortality, diagnostic upper GI endoscopy is affected by a substantial miss rate of neoplastic lesions due to failure to recognize a visible lesion or imperfect navigation. This may be offset by the real-time application of artificial intelligence (AI) for detection (computer-aided detection [CADe]) and characterization (computer-aided diagnosis [CADx]) of upper GI neoplasia. Stand-alone performance of CADe for esophageal squamous cell neoplasia, Barrett's esophagus-related neoplasia, and gastric cancer showed promising accuracy, sensitivity ranging between 83% and 93%. However, incorporation of CADe/CADx in clinical practice depends on several factors, such as possible bias in the training or validation phases of these algorithms, its interaction with human endoscopists, and clinical implications of false-positive results. The aim of this review is to guide the clinician across the multiple steps of AI development in clinical practice.
Palavras-chave
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Esôfago de Barrett
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Neoplasias Esofágicas
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Trato Gastrointestinal Superior
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Aprendizado Profundo
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Neoplasias Gastrointestinais
Limite:
Humans
Idioma:
En
Revista:
Gastroenterology
Ano de publicação:
2022
Tipo de documento:
Article