Literature DB >> 27381057

Computer-Aided Diagnosis of Different Rotator Cuff Lesions Using Shoulder Musculoskeletal Ultrasound.

Ruey-Feng Chang1, Chung-Chien Lee2, Chung-Ming Lo3.   

Abstract

The lifetime prevalence of shoulder pain approaches 70%, which is mostly attributable to rotator cuff lesions such as inflammation, calcific tendinitis and tears. On clinical examination, shoulder ultrasound is recommended for the detection of lesions. However, there exists inter-operator variability in diagnostic accuracy because of differences in the experience and expertise of operators. In this study, a computer-aided diagnosis (CAD) system was developed to assist ultrasound operators in diagnosing rotator cuff lesions and to improve the practicality of ultrasound examination. The collected cases included 43 cases of inflammation, 30 cases of calcific tendinitis and 26 tears. For each case, the lesion area and texture features were extracted from the entire lesions and combined in a multinomial logistic regression classifier for lesion classification. The proposed CAD achieved an accuracy of 87.9%. The individual accuracy of this CAD system was 88.4% for inflammation, 83.3% for calcific tendinitis and 92.3% for tears. Cohen's k was 0.798. On the basis of its diagnostic performance, clinical use of this CAD technique has promise.
Copyright © 2016 World Federation for Ultrasound in Medicine & Biology. Published by Elsevier Inc. All rights reserved.

Entities:  

Keywords:  Computer-aided diagnosis; Rotator cuff lesions; Shoulder ultrasound; Texture

Mesh:

Year:  2016        PMID: 27381057     DOI: 10.1016/j.ultrasmedbio.2016.05.016

Source DB:  PubMed          Journal:  Ultrasound Med Biol        ISSN: 0301-5629            Impact factor:   2.998


  7 in total

Review 1.  Computer-aided diagnosis in rheumatic diseases using ultrasound: an overview.

Authors:  Josefina Gutiérrez-Martínez; Carlos Pineda; Hugo Sandoval; Araceli Bernal-González
Journal:  Clin Rheumatol       Date:  2019-11-06       Impact factor: 2.980

Review 2.  Detecting Rotator Cuff Tears: A Network Meta-analysis of 144 Diagnostic Studies.

Authors:  Fanxiao Liu; Jinlei Dong; Wun-Jer Shen; Qinglin Kang; Dongsheng Zhou; Fei Xiong
Journal:  Orthop J Sports Med       Date:  2020-02-05

3.  Artificial Intelligence for Ultrasound Informative Image Selection of Metacarpal Head Cartilage. A Pilot Study.

Authors:  Edoardo Cipolletta; Maria Chiara Fiorentino; Sara Moccia; Irene Guidotti; Walter Grassi; Emilio Filippucci; Emanuele Frontoni
Journal:  Front Med (Lausanne)       Date:  2021-03-01

4.  Construction of a Computer-Aided Analysis System for Orthopedic Diseases Based on High-Frequency Ultrasound Images.

Authors:  Feifei Xiu; Guishan Rong; Tao Zhang
Journal:  Comput Math Methods Med       Date:  2022-01-05       Impact factor: 2.238

5.  Development of a convolutional neural network for the identification and the measurement of the median nerve on ultrasound images acquired at carpal tunnel level.

Authors:  Gianluca Smerilli; Edoardo Cipolletta; Gianmarco Sartini; Erica Moscioni; Mariachiara Di Cosmo; Maria Chiara Fiorentino; Sara Moccia; Emanuele Frontoni; Walter Grassi; Emilio Filippucci
Journal:  Arthritis Res Ther       Date:  2022-02-08       Impact factor: 5.156

6.  Imbalanced Loss-Integrated Deep-Learning-Based Ultrasound Image Analysis for Diagnosis of Rotator-Cuff Tear.

Authors:  Kyungsu Lee; Jun Young Kim; Moon Hwan Lee; Chang-Hyuk Choi; Jae Youn Hwang
Journal:  Sensors (Basel)       Date:  2021-03-22       Impact factor: 3.576

7.  Computer-aided diagnosis of isocitrate dehydrogenase genotypes in glioblastomas from radiomic patterns.

Authors:  Chung-Ming Lo; Rui-Cian Weng; Sho-Jen Cheng; Hung-Jung Wang; Kevin Li-Chun Hsieh
Journal:  Medicine (Baltimore)       Date:  2020-02       Impact factor: 1.817

  7 in total

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