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

被引:10
|
作者
Gutierrez-Martinez, Josefina [1 ]
Pineda, Carlos [2 ]
Sandoval, Hugo [3 ]
Bernal-Gonzalez, Araceli [2 ]
机构
[1] Inst Nacl Rehabil Luis Guillermo Ibarra Ibarra, Div Med Engn Res, Calzada Mexico Xochimilco 289, Mexico City 14389, DF, Mexico
[2] Inst Nacl Rehabil Luis Guillermo Ibarra Ibarra, Div Musculoskeletal & Rheumat Disorders, Calzada Mexico Xochimilco 289, Mexico City 14389, DF, Mexico
[3] Inst Nacl Rehabil Luis Guillermo Ibarra Ibarra, Sociomed Res Unit, Calzada Mexico Xochimilco 289, Mexico City 14389, DF, Mexico
关键词
Artificial intelligence; Computer-assisted diagnosis; Expert systems; Machine learning; Rheumatology; X-RAY; SYSTEM; OSTEOARTHRITIS; ARTHRITIS; IMAGES; CLASSIFICATION; VALIDATION; CRITERIA;
D O I
10.1007/s10067-019-04791-z
中图分类号
R5 [内科学];
学科分类号
1002 ; 100201 ;
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:13
相关论文
共 50 条
  • [21] BUS-BRA: A breast ultrasound dataset for assessing computer-aided diagnosis systems
    Gomez-Flores, Wilfrido
    Gregorio-Calas, Maria Julia
    Pereira, Wagner Coelho de Albuquerque
    MEDICAL PHYSICS, 2024, 51 (04) : 3110 - 3123
  • [22] Accuracy of Computer-Aided Diagnosis of Melanoma A Meta-analysis
    Dick, Vincent
    Sinz, Christoph
    Mittlboeck, Martina
    Kittler, Harald
    Tschandl, Philipp
    JAMA DERMATOLOGY, 2019, 155 (11) : 1291 - 1299
  • [23] Computer-aided diagnosis in the era of deep learning
    Chan, Heang-Ping
    Hadjiiski, Lubomir M.
    Samala, Ravi K.
    MEDICAL PHYSICS, 2020, 47 (05) : E218 - E227
  • [24] Developments in the detection of diabetic retinopathy: a state-of-the-art review of computer-aided diagnosis and machine learning methods
    Selvachandran, Ganeshsree
    Quek, Shio Gai
    Paramesran, Raveendran
    Ding, Weiping
    Son, Le Hoang
    ARTIFICIAL INTELLIGENCE REVIEW, 2023, 56 (02) : 915 - 964
  • [25] Computer-Aided Diagnosis of Mammographic Masses Using Scalable Image Retrieval
    Jiang, Menglin
    Zhang, Shaoting
    Li, Hongsheng
    Metaxas, Dimitris N.
    IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING, 2015, 62 (02) : 783 - 792
  • [26] Computer-Aided Detection and Diagnosis of Neurological Disorder
    Huse, Shreyash
    Acharya, Sourya
    Shukla, Samarth
    Harshita, J.
    Sachdev, Ankita
    CUREUS JOURNAL OF MEDICAL SCIENCE, 2022, 14 (08)
  • [27] Computer-Aided Diagnosis Based on Extreme Learning Machine: A Review
    Wang, Zhiqiong
    Luo, Yiqi
    Xin, Junchang
    Zhang, Hao
    Qu, Luxuan
    Wang, Zhongyang
    Yao, Yudong
    Zhu, Wancheng
    Wang, Xingwei
    IEEE ACCESS, 2020, 8 : 141657 - 141673
  • [28] Computer-Aided Diagnosis of Malignant Mammograms using Zernike Moments and SVM
    Sharma, Shubhi
    Khanna, Pritee
    JOURNAL OF DIGITAL IMAGING, 2015, 28 (01) : 77 - 90
  • [29] An Ultrasound-Based Computer-Aided Diagnosis Tool for Steatosis Detection
    Ribeiro, Ricardo T.
    Marinho, Rui Tato
    Miguel Sanches, J.
    IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS, 2014, 18 (04) : 1397 - 1403
  • [30] A Review of Computer-Aided Breast Cancer Diagnosis Using Sequential Mammograms
    Loizidou, Kosmia
    Skouroumouni, Galateia
    Nikolaou, Christos
    Pitris, Costas
    TOMOGRAPHY, 2022, 8 (06) : 2874 - 2892