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A survey on Barrett's esophagus analysis using machine learning

de Souza, Luis A. ; Palm, Christoph ; Mendel, Robert ; Hook, Christian ; Ebigbo, Alanna ; Probst, Andreas ; Messmann, Helmut ; Weber, Silke ; Papa, João P.

Computers in biology and medicine, 2018-05, Vol.96, p.203-213 [Periódico revisado por pares]

United States: Elsevier Ltd

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Título:
A survey on Barrett's esophagus analysis using machine learning
Autor: de Souza, Luis A. ; Palm, Christoph ; Mendel, Robert ; Hook, Christian ; Ebigbo, Alanna ; Probst, Andreas ; Messmann, Helmut ; Weber, Silke ; Papa, João P.
Assuntos: Accuracy ; Adenocarcinoma ; Artificial intelligence ; Automation ; Barrett's esophagus ; Biopsy ; Cancer ; Classification ; Computer applications ; Computer-aided diagnosis ; Diagnosis ; Dysplasia ; Electronic publishing ; Endoscopes ; Endoscopy ; Esophagus ; Experts ; Health surveillance ; Histology ; Image processing ; Learning algorithms ; Machine learning ; Medical diagnosis ; Medical imaging ; Methods ; Pattern recognition ; Segmentation ; Tomography
É parte de: Computers in biology and medicine, 2018-05, Vol.96, p.203-213
Notas: ObjectType-Article-2
SourceType-Scholarly Journals-1
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ObjectType-Feature-3
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Descrição: This work presents a systematic review concerning recent studies and technologies of machine learning for Barrett's esophagus (BE) diagnosis and treatment. The use of artificial intelligence is a brand new and promising way to evaluate such disease. We compile some works published at some well-established databases, such as Science Direct, IEEEXplore, PubMed, Plos One, Multidisciplinary Digital Publishing Institute (MDPI), Association for Computing Machinery (ACM), Springer, and Hindawi Publishing Corporation. Each selected work has been analyzed to present its objective, methodology, and results. The BE progression to dysplasia or adenocarcinoma shows a complex pattern to be detected during endoscopic surveillance. Therefore, it is valuable to assist its diagnosis and automatic identification using computer analysis. The evaluation of the BE dysplasia can be performed through manual or automated segmentation through machine learning techniques. Finally, in this survey, we reviewed recent studies focused on the automatic detection of the neoplastic region for classification purposes using machine learning methods. •Compilation of works that deal with Barrett's esophagus (BE) identification.•A recent update of the related literature.•Summarization of Works, datasets, and classification techniques used.
Editor: United States: Elsevier Ltd
Idioma: Inglês

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A survey on Barrett's esophagus analysis using machine learning

de Souza, Luis A. ; Palm, Christoph ; Mendel, Robert ; Hook, Christian ; Ebigbo, Alanna ; Probst, Andreas ; Messmann, Helmut ; Weber, Silke ; Papa, João P.

United States: Elsevier Ltd

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