Cargando…
Quantitative diagnosis of rotator cuff tears based on sonographic pattern recognition
The lifetime prevalence of shoulder pain is nearly 70% and is mostly attributable to subacromial disorders. A rotator cuff tear is the most severe form of subacromial disorders, and most occur in the supraspinatus. For clinical examination, shoulder ultrasound is recommended to detect supraspinatus...
Autores principales: | , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2019
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6394937/ https://www.ncbi.nlm.nih.gov/pubmed/30817804 http://dx.doi.org/10.1371/journal.pone.0212741 |
_version_ | 1783398989988626432 |
---|---|
author | Chang, Ruey-Feng Lee, Chung-Chien Lo, Chung-Ming |
author_facet | Chang, Ruey-Feng Lee, Chung-Chien Lo, Chung-Ming |
author_sort | Chang, Ruey-Feng |
collection | PubMed |
description | The lifetime prevalence of shoulder pain is nearly 70% and is mostly attributable to subacromial disorders. A rotator cuff tear is the most severe form of subacromial disorders, and most occur in the supraspinatus. For clinical examination, shoulder ultrasound is recommended to detect supraspinatus tears. In this study, a computer-aided tear classification (CTC) system was developed to identify supraspinatus tears in ultrasound examinations and reduce inter-operator variability. The observed cases included 89 ultrasound images of supraspinatus tendinopathy and 102 of supraspinatus tear from 136 patients. For each case, intensity and texture features were extracted from the entire lesion and combined in a binary logistic regression classifier for lesion classification. The proposed CTC system achieved an accuracy rate of 92% (176/191) and an area under receiver operating characteristic curve (Az) of 0.9694. Based on its diagnostic performance, the CTC system has promise for clinical use. |
format | Online Article Text |
id | pubmed-6394937 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-63949372019-03-08 Quantitative diagnosis of rotator cuff tears based on sonographic pattern recognition Chang, Ruey-Feng Lee, Chung-Chien Lo, Chung-Ming PLoS One Research Article The lifetime prevalence of shoulder pain is nearly 70% and is mostly attributable to subacromial disorders. A rotator cuff tear is the most severe form of subacromial disorders, and most occur in the supraspinatus. For clinical examination, shoulder ultrasound is recommended to detect supraspinatus tears. In this study, a computer-aided tear classification (CTC) system was developed to identify supraspinatus tears in ultrasound examinations and reduce inter-operator variability. The observed cases included 89 ultrasound images of supraspinatus tendinopathy and 102 of supraspinatus tear from 136 patients. For each case, intensity and texture features were extracted from the entire lesion and combined in a binary logistic regression classifier for lesion classification. The proposed CTC system achieved an accuracy rate of 92% (176/191) and an area under receiver operating characteristic curve (Az) of 0.9694. Based on its diagnostic performance, the CTC system has promise for clinical use. Public Library of Science 2019-02-28 /pmc/articles/PMC6394937/ /pubmed/30817804 http://dx.doi.org/10.1371/journal.pone.0212741 Text en © 2019 Chang et al http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Research Article Chang, Ruey-Feng Lee, Chung-Chien Lo, Chung-Ming Quantitative diagnosis of rotator cuff tears based on sonographic pattern recognition |
title | Quantitative diagnosis of rotator cuff tears based on sonographic pattern recognition |
title_full | Quantitative diagnosis of rotator cuff tears based on sonographic pattern recognition |
title_fullStr | Quantitative diagnosis of rotator cuff tears based on sonographic pattern recognition |
title_full_unstemmed | Quantitative diagnosis of rotator cuff tears based on sonographic pattern recognition |
title_short | Quantitative diagnosis of rotator cuff tears based on sonographic pattern recognition |
title_sort | quantitative diagnosis of rotator cuff tears based on sonographic pattern recognition |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6394937/ https://www.ncbi.nlm.nih.gov/pubmed/30817804 http://dx.doi.org/10.1371/journal.pone.0212741 |
work_keys_str_mv | AT changrueyfeng quantitativediagnosisofrotatorcufftearsbasedonsonographicpatternrecognition AT leechungchien quantitativediagnosisofrotatorcufftearsbasedonsonographicpatternrecognition AT lochungming quantitativediagnosisofrotatorcufftearsbasedonsonographicpatternrecognition |