A survey on Barrett's esophagus analysis using machine learning

Autor(es)

de Souza Jr, Luis A. ; Palm, Christoph ^{[1, 2, 3]} ; Mendel, Robert ^[1] ; Hook, Christian ^[1] ; Ebigbo, Alanna ^[4] ; Probst, Andreas ^[4] ; Messmann, Helmut ^[4] ; Weber, Silke ^[5] ; Papa, Joao P. ^[6]

Afiliação do(s) autor(es)

^[1] de Souza Jr, Jr., Luis A., Ostbayer Tech Hsch Regensburg OTH Regensburg, Regensburg Med Image Comp ReMIC, Regensburg - Germany

^[2] OTH Regensburg, RCBE, Regensburg - Germany

^[3] Regensburg Univ, Regensburg - Germany

^[4] Klinikum Augsburg, Med Klin 3, Augsburg - Germany

^[5] Sao Paulo State Univ, Dept Otorhinolaryngol, Sao Paulo - Brazil

^[6] de Souza Jr, Jr., Luis A., Sao Paulo State Univ, UNESP, Dept Comp, Sao Paulo - Brazil

• Tipo de documento: Artigo Científico
• Fonte: COMPUTERS IN BIOLOGY AND MEDICINE; v. 96, p. 203-213, MAY 1 2018.
• Citações Web of Science: 6

Resumo

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. (AU)

• Processo FAPESP: 13/07375-0 - CeMEAI - Centro de Ciências Matemáticas Aplicadas à Indústria
• Beneficiário: Francisco Louzada Neto
• Modalidade de apoio: Auxílio à Pesquisa - Centros de Pesquisa, Inovação e Difusão - CEPIDs
• Processo FAPESP: 14/12236-1 - AnImaLS: Anotação de Imagem em Larga Escala: o que máquinas e especialistas podem aprender interagindo?
• Beneficiário: Alexandre Xavier Falcão
• Modalidade de apoio: Auxílio à Pesquisa - Temático
• Processo FAPESP: 16/19403-6 - Modelos de aprendizado baseados em energia e suas aplicações
• Beneficiário: João Paulo Papa
• Modalidade de apoio: Auxílio à Pesquisa - Regular