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Computer-Aided Ankle Ligament Injury Diagnosis from Magnetic Resonance Images Using Machine Learning Techniques.
Astolfi, Rodrigo S; da Silva, Daniel S; Guedes, Ingrid S; Nascimento, Caio S; Damasevicius, Robertas; Jagatheesaperumal, Senthil K; de Albuquerque, Victor Hugo C; Leite, José Alberto D.
Afiliação
  • Astolfi RS; Graduate Program in Surgery, Federal University of Ceará, Fortaleza 60455-970, CE, Brazil.
  • da Silva DS; Department of Teleinformatics Engineering, Federal University of Ceará, Fortaleza 60455-970, CE, Brazil.
  • Guedes IS; Graduate Program in Surgery, Federal University of Ceará, Fortaleza 60455-970, CE, Brazil.
  • Nascimento CS; Department of Teleinformatics Engineering, Federal University of Ceará, Fortaleza 60455-970, CE, Brazil.
  • Damasevicius R; Department of Software Engineering, Kaunas University of Technology, 51368 Kaunas, Lithuania.
  • Jagatheesaperumal SK; Department of Electronics and Communication Engineering, Mepco Schlenk Engineering College, Sivakasi 626005, TN, India.
  • de Albuquerque VHC; Department of Teleinformatics Engineering, Federal University of Ceará, Fortaleza 60455-970, CE, Brazil.
  • Leite JAD; Graduate Program in Surgery, Federal University of Ceará, Fortaleza 60455-970, CE, Brazil.
Sensors (Basel) ; 23(3)2023 Feb 01.
Article em En | MEDLINE | ID: mdl-36772604
Ankle injuries caused by the Anterior Talofibular Ligament (ATFL) are the most common type of injury. Thus, finding new ways to analyze these injuries through novel technologies is critical for assisting medical diagnosis and, as a result, reducing the subjectivity of this process. As a result, the purpose of this study is to compare the ability of specialists to diagnose lateral tibial tuberosity advancement (LTTA) injury using computer vision analysis on magnetic resonance imaging (MRI). The experiments were carried out on a database obtained from the Vue PACS-Carestream software, which contained 132 images of ATFL and normal (healthy) ankles. Because there were only a few images, image augmentation techniques was used to increase the number of images in the database. Following that, various feature extraction algorithms (GLCM, LBP, and HU invariant moments) and classifiers such as Multi-Layer Perceptron (MLP), Support Vector Machine (SVM), k-Nearest Neighbors (kNN), and Random Forest (RF) were used. Based on the results from this analysis, for cases that lack clear morphologies, the method delivers a hit rate of 85.03% with an increase of 22% over the human expert-based analysis.
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Traumatismos do Tornozelo / Ligamentos Laterais do Tornozelo Tipo de estudo: Diagnostic_studies Limite: Humans Idioma: En Revista: Sensors (Basel) Ano de publicação: 2023 Tipo de documento: Article País de afiliação: Brasil País de publicação: Suíça
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: Traumatismos do Tornozelo / Ligamentos Laterais do Tornozelo Tipo de estudo: Diagnostic_studies Limite: Humans Idioma: En Revista: Sensors (Basel) Ano de publicação: 2023 Tipo de documento: Article País de afiliação: Brasil País de publicação: Suíça