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A systematic review and repeatability study on the use of deep learning for classifying and detecting tuberculosis bacilli in microscopic images.
Mota Carvalho, Thales Francisco; Santos, Vívian Ludimila Aguiar; Silva, Jose Cleydson Ferreira; Figueredo, Lida Jouca de Assis; de Miranda, Silvana Spíndola; Duarte, Ricardo de Oliveira; Guimarães, Frederico Gadelha.
Afiliação
  • Mota Carvalho TF; Electrical Engineering, Universidade Federal de Minas Gerais, Av. Antônio Carlos 6627, Belo Horizonte, 31270-901, MG, Brazil; Institute of Engineering, Science and Technology, Universidade Federal dos Vales do Jequitinhonha e Mucuri, Av. Um, 4.050, Janaúba, 39447-814, MG, Brazil.
  • Santos VLA; Institute of Engineering, Science and Technology, Universidade Federal dos Vales do Jequitinhonha e Mucuri, Av. Um, 4.050, Janaúba, 39447-814, MG, Brazil; Instituto Federal do Norte Minas Gerais, Rua Humberto Mallard 1355, Pirapora, 39274-140, MG, Brazil.
  • Silva JCF; Horticultural Sciences Department, University of Florida, Gainesville, FL, USA.
  • Figueredo LJA; Faculdade de Medicina, Laboratório de pesquisa em micobactérias, Universidade Federal de Minas Gerais, Av. Alfredo Balena, 190, Belo Horizonte, 30130-100, MG, Brazil.
  • de Miranda SS; Faculdade de Medicina, Laboratório de pesquisa em micobactérias, Universidade Federal de Minas Gerais, Av. Alfredo Balena, 190, Belo Horizonte, 30130-100, MG, Brazil.
  • Duarte RO; Department of Electronics, Universidade Federal de Minas Gerais, Av. Antônio Carlos, 6627, Belo Horizonte, MG, Brazil.
  • Guimarães FG; Machine Intelligence and Data Science (MINDS) Laboratory, Department of Electrical Engineering, Universidade Federal de Minas Gerais, Av. Antônio Carlos 6627, Belo Horizonte, 31270-901, MG, Brazil. Electronic address: fredericoguimaraes@ufmg.br.
Prog Biophys Mol Biol ; 180-181: 1-18, 2023.
Article em En | MEDLINE | ID: mdl-37023799
Tuberculosis (TB) is among the leading causes of death worldwide from a single infectious agent. This disease usually affects the lungs (pulmonary TB) and can be cured in most cases with a quick diagnosis and proper treatment. Microscopic sputum smear is widely used to diagnose and manage pulmonary TB. Despite being relatively fast and low cost, it can be exhausting because it depends on manually counting TB bacilli (Mycobacterium tuberculosis) in microscope images. In this context, different Deep Learning (DL) techniques are proposed in the literature to assist in performing smear microscopy. This article presents a systematic review based on the PRISMA procedure, which investigates which DL techniques can contribute to classifying TB bacilli in microscopic images of sputum smears using the Ziehl-Nielsen method. After an extensive search and a careful inclusion/exclusion procedure, 28 papers were selected from a total of 400 papers retrieved from nine databases. Based on these articles, the DL techniques are presented as possible solutions to improve smear microscopy. The main concepts necessary to understand how such techniques are proposed and used are also presented. In addition, replication work is also carried out, verifying reproducibility and comparing different works in the literature. In this review, we look at how DL techniques can be a partner to make sputum smear microscopy faster and more efficient. We also identify some gaps in the literature that can guide which issues can be addressed in other works to contribute to the practical use of these methods in laboratories.
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Tuberculose / Tuberculose Pulmonar / Aprendizado Profundo / Mycobacterium tuberculosis Tipo de estudo: Diagnostic_studies / Prognostic_studies / Systematic_reviews Limite: Humans Idioma: En Revista: Prog Biophys Mol Biol Ano de publicação: 2023 Tipo de documento: Article País de afiliação: Brasil País de publicação: Reino Unido
Texto completo
Adicionar na Minha BVS
Imprimir
XML
PubMed Links
Buscar no Google

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Tuberculose / Tuberculose Pulmonar / Aprendizado Profundo / Mycobacterium tuberculosis Tipo de estudo: Diagnostic_studies / Prognostic_studies / Systematic_reviews Limite: Humans Idioma: En Revista: Prog Biophys Mol Biol Ano de publicação: 2023 Tipo de documento: Article País de afiliação: Brasil País de publicação: Reino Unido