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

被引：0
作者：
Josefina Gutiérrez-Martínez
Carlos Pineda
Hugo Sandoval
Araceli Bernal-González
机构：
[1] Instituto Nacional de Rehabilitación Luis Guillermo Ibarra Ibarra,Division of Medical Engineering Research
[2] Instituto Nacional de Rehabilitación Luis Guillermo Ibarra Ibarra,Division of Musculoskeletal and Rheumatic Disorders
[3] Instituto Nacional de Rehabilitación Luis Guillermo Ibarra Ibarra,Sociomedical Research Unit
来源：
Clinical Rheumatology | 2020年 / 39卷
关键词：
Artificial intelligence; Computer-assisted diagnosis; Expert systems; Machine learning; Rheumatology;
D O I：
暂无
中图分类号：
学科分类号：
摘要：
Clinical evaluation of rheumatic and musculoskeletal diseases through images is a challenge for the beginner rheumatologist since image diagnosis is an expert task with a long learning curve. The aim of this work was to present a narrative review on the main ultrasound computer-aided diagnosis systems that may help clinicians thanks to the progress made in the application of artificial intelligence techniques. We performed a literature review searching for original articles in seven repositories, from 1970 to 2019, and identified 11 main methods currently used in ultrasound computer-aided diagnosis systems. Also, we found that rheumatoid arthritis, osteoarthritis, systemic lupus erythematosus, and idiopathic inflammatory myopathies are the four musculoskeletal and rheumatic diseases most studied that use these innovative systems, with an overall accuracy of > 75%.
引用
收藏
页码：993 / 1005
页数：12
相关论文
共 257 条
[21]  
Do Prado AD(2011)Writing a narrative biomedical review: considerations for authors, peer reviewers, and editors Rheumatol Int 31 1409-194
[22]  
Staub HL(2012)Improved detection of synovial boundaries in ultrasound examination by using a cascade of active-contours Med Eng Phys 35 188-155
[23]  
Bisi MC(2018)Machine learning in ultrasound computer-aided diagnostic systems: a survey Biomed Res Int 2018 5137904-2449
[24]  
da Silveira IG(2013)Automated image analysis of skeletal muscle fiber cross-sectional area J Appl Physiol 114 148-651
[25]  
Mendonca JA(2017)Deep convolutional neural networks for image classification: a comprehensive review Neural Comput 29 2352-10937
[26]  
Polido-Pereira J(1970)Experience counting in sequential computer diagnosis JAMA Intern Med 126 647-2250
[27]  
Fonseca JE(2018)Automatic identification of bone erosions in rheumatoid arthritis from hand radiographs based on deep convolutional neural network Multimed Tools Appl 77 10921-1187
[28]  
Ostendorf B(2017)Predicting early symptomatic osteoarthritis in the human knee using machine learning classification of magnetic resonance images from the osteoarthritis initiative J Orthop Res 35 2243-336
[29]  
Mattes-Gyorgy K(2017)Computer-based automated analysis of X-ray and thermal imaging of knee region in evaluation of rheumatoid arthritis Proc Inst Mech Eng H J Eng Med 231 1178-39
[30]  
Reichelt DC(1989)MESICAR-A medical expert system integrating causal and associative reasoning Appl Artif Intell 3 305-914
12345678910