Cargando…

COMPUTATIONAL ANALYSIS BASED ON ARTIFICIAL NEURAL NETWORKS FOR AIDING IN DIAGNOSING OSTEOARTHRITIS OF THE LUMBAR SPINE

Objective: To ascertain the advantages of applying artificial neural networks to recognize patterns on lumbar spine radiographies in order to aid in the process of diagnosing primary osteoarthritis. Methods: This was a cross-sectional descriptive analytical study with a quantitative approach and an...

Descripción completa

Detalles Bibliográficos
Autores principales: Veronezi, Carlos Cassiano Denipotti, de Azevedo Simões, Priscyla Waleska Targino, dos Santos, Robson Luiz, da Rocha, Edroaldo Lummertz, Meláo, Suelen, de Mattos, Merisandra Côrtes, Cechinel, Cristian
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2015
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4799207/
https://www.ncbi.nlm.nih.gov/pubmed/27027010
http://dx.doi.org/10.1016/S2255-4971(15)30239-1
_version_ 1782422296611258368
author Veronezi, Carlos Cassiano Denipotti
de Azevedo Simões, Priscyla Waleska Targino
dos Santos, Robson Luiz
da Rocha, Edroaldo Lummertz
Meláo, Suelen
de Mattos, Merisandra Côrtes
Cechinel, Cristian
author_facet Veronezi, Carlos Cassiano Denipotti
de Azevedo Simões, Priscyla Waleska Targino
dos Santos, Robson Luiz
da Rocha, Edroaldo Lummertz
Meláo, Suelen
de Mattos, Merisandra Côrtes
Cechinel, Cristian
author_sort Veronezi, Carlos Cassiano Denipotti
collection PubMed
description Objective: To ascertain the advantages of applying artificial neural networks to recognize patterns on lumbar spine radiographies in order to aid in the process of diagnosing primary osteoarthritis. Methods: This was a cross-sectional descriptive analytical study with a quantitative approach and an emphasis on diagnosis. The training set was composed of images collected between January and July 2009 from patients who had undergone lateral-view digital radiographies of the lumbar spine, which were provided by a radiology clinic located in the municipality of Criciúma (SC). Out of the total of 260 images gathered, those with distortions, those presenting pathological conditions that altered the architecture of the lumbar spine and those with patterns that were difficult to characterize were discarded, resulting in 206 images. The image data base (n = 206) was then subdivided, resulting in 68 radiographies for the training stage, 68 images for tests and 70 for validation. A hybrid neural network based on Kohonen self-organizing maps and on Multilayer Perceptron networks was used. Results: After 90 cycles, the validation was carried out on the best results, achieving accuracy of 62.85%, sensitivity of 65.71% and specificity of 60%. Conclusions: Even though the effectiveness shown was moderate, this study is still innovative. The values show that the technique used has a promising future, pointing towards further studies on image and cycle processing methodology with a larger quantity of radiographies.
format Online
Article
Text
id pubmed-4799207
institution National Center for Biotechnology Information
language English
publishDate 2015
publisher Elsevier
record_format MEDLINE/PubMed
spelling pubmed-47992072016-03-29 COMPUTATIONAL ANALYSIS BASED ON ARTIFICIAL NEURAL NETWORKS FOR AIDING IN DIAGNOSING OSTEOARTHRITIS OF THE LUMBAR SPINE Veronezi, Carlos Cassiano Denipotti de Azevedo Simões, Priscyla Waleska Targino dos Santos, Robson Luiz da Rocha, Edroaldo Lummertz Meláo, Suelen de Mattos, Merisandra Côrtes Cechinel, Cristian Rev Bras Ortop Original Article Objective: To ascertain the advantages of applying artificial neural networks to recognize patterns on lumbar spine radiographies in order to aid in the process of diagnosing primary osteoarthritis. Methods: This was a cross-sectional descriptive analytical study with a quantitative approach and an emphasis on diagnosis. The training set was composed of images collected between January and July 2009 from patients who had undergone lateral-view digital radiographies of the lumbar spine, which were provided by a radiology clinic located in the municipality of Criciúma (SC). Out of the total of 260 images gathered, those with distortions, those presenting pathological conditions that altered the architecture of the lumbar spine and those with patterns that were difficult to characterize were discarded, resulting in 206 images. The image data base (n = 206) was then subdivided, resulting in 68 radiographies for the training stage, 68 images for tests and 70 for validation. A hybrid neural network based on Kohonen self-organizing maps and on Multilayer Perceptron networks was used. Results: After 90 cycles, the validation was carried out on the best results, achieving accuracy of 62.85%, sensitivity of 65.71% and specificity of 60%. Conclusions: Even though the effectiveness shown was moderate, this study is still innovative. The values show that the technique used has a promising future, pointing towards further studies on image and cycle processing methodology with a larger quantity of radiographies. Elsevier 2015-12-06 /pmc/articles/PMC4799207/ /pubmed/27027010 http://dx.doi.org/10.1016/S2255-4971(15)30239-1 Text en © 2011 Sociedade Brasileira de Ortopedia e Traumatologia http://creativecommons.org/licenses/by-nc-nd/4.0/ This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
spellingShingle Original Article
Veronezi, Carlos Cassiano Denipotti
de Azevedo Simões, Priscyla Waleska Targino
dos Santos, Robson Luiz
da Rocha, Edroaldo Lummertz
Meláo, Suelen
de Mattos, Merisandra Côrtes
Cechinel, Cristian
COMPUTATIONAL ANALYSIS BASED ON ARTIFICIAL NEURAL NETWORKS FOR AIDING IN DIAGNOSING OSTEOARTHRITIS OF THE LUMBAR SPINE
title COMPUTATIONAL ANALYSIS BASED ON ARTIFICIAL NEURAL NETWORKS FOR AIDING IN DIAGNOSING OSTEOARTHRITIS OF THE LUMBAR SPINE
title_full COMPUTATIONAL ANALYSIS BASED ON ARTIFICIAL NEURAL NETWORKS FOR AIDING IN DIAGNOSING OSTEOARTHRITIS OF THE LUMBAR SPINE
title_fullStr COMPUTATIONAL ANALYSIS BASED ON ARTIFICIAL NEURAL NETWORKS FOR AIDING IN DIAGNOSING OSTEOARTHRITIS OF THE LUMBAR SPINE
title_full_unstemmed COMPUTATIONAL ANALYSIS BASED ON ARTIFICIAL NEURAL NETWORKS FOR AIDING IN DIAGNOSING OSTEOARTHRITIS OF THE LUMBAR SPINE
title_short COMPUTATIONAL ANALYSIS BASED ON ARTIFICIAL NEURAL NETWORKS FOR AIDING IN DIAGNOSING OSTEOARTHRITIS OF THE LUMBAR SPINE
title_sort computational analysis based on artificial neural networks for aiding in diagnosing osteoarthritis of the lumbar spine
topic Original Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4799207/
https://www.ncbi.nlm.nih.gov/pubmed/27027010
http://dx.doi.org/10.1016/S2255-4971(15)30239-1
work_keys_str_mv AT veronezicarloscassianodenipotti computationalanalysisbasedonartificialneuralnetworksforaidingindiagnosingosteoarthritisofthelumbarspine
AT deazevedosimoespriscylawaleskatargino computationalanalysisbasedonartificialneuralnetworksforaidingindiagnosingosteoarthritisofthelumbarspine
AT dossantosrobsonluiz computationalanalysisbasedonartificialneuralnetworksforaidingindiagnosingosteoarthritisofthelumbarspine
AT darochaedroaldolummertz computationalanalysisbasedonartificialneuralnetworksforaidingindiagnosingosteoarthritisofthelumbarspine
AT melaosuelen computationalanalysisbasedonartificialneuralnetworksforaidingindiagnosingosteoarthritisofthelumbarspine
AT demattosmerisandracortes computationalanalysisbasedonartificialneuralnetworksforaidingindiagnosingosteoarthritisofthelumbarspine
AT cechinelcristian computationalanalysisbasedonartificialneuralnetworksforaidingindiagnosingosteoarthritisofthelumbarspine