Automated Classification of Rheumatoid Arthritis, Osteoarthritis, and Normal Hand Radiographs with Deep Learning Methods.

Rheumatoid arthritis and hand osteoarthritis are two different arthritis that causes pain, function limitation, and permanent joint damage in the hands. Plain hand radiographs are the most commonly used imaging methods for the diagnosis, differential diagnosis, and monitoring of rheumatoid arthritis...

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Publicado en:Journal of Digital Imaging Vol. 35; no. 2; pp. 193 - 200
Autores principales: Üreten, Kemal, Maraş, Hadi Hakan
Formato: diagnostic images equations & formulas research tables/charts Journal Article
Publicado: Springer Nature Apr2022
Acceso en línea:Ver este registro en EBSCOhost
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      dt: Apr2022
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      pub: Springer Nature
      place: New York, New York
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        atl: Automated Classification of Rheumatoid Arthritis, Osteoarthritis, and Normal Hand Radiographs with Deep Learning Methods.
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        au:
          Üreten, Kemal
          Maraş, Hadi Hakan
        affil: Department of Rheumatology, Faculty of Medicine, Kırıkkale University, 71450, Kırıkkale, Turkey
      sug:
        subj:
          Arthritis, Rheumatoid Diagnosis
          Osteoarthritis, Hand Diagnosis
          Hand Joints Radiography
          Deep Learning
          Diagnosis, Computer Assisted
          Sensitivity and Specificity
          Human
          Retrospective Design
          Record Review
          Algorithms
          ROC Curve
          Descriptive Statistics
          Digital Imaging
      ab: Rheumatoid arthritis and hand osteoarthritis are two different arthritis that causes pain, function limitation, and permanent joint damage in the hands. Plain hand radiographs are the most commonly used imaging methods for the diagnosis, differential diagnosis, and monitoring of rheumatoid arthritis and osteoarthritis. In this retrospective study, the You Only Look Once (YOLO) algorithm was used to obtain hand images from original radiographs without data loss, and classification was made by applying transfer learning with a pre-trained VGG-16 network. The data augmentation method was applied during training. The results of the study were evaluated with performance metrics such as accuracy, sensitivity, specificity, and precision calculated from the confusion matrix, and AUC (area under the ROC curve) calculated from ROC (receiver operating characteristic) curve. In the classification of rheumatoid arthritis and normal hand radiographs, 90.7%, 92.6%, 88.7%, 89.3%, and 0.97 accuracy, sensitivity, specificity, precision, and AUC results, respectively, and in the classification of osteoarthritis and normal hand radiographs, 90.8%, 91.4%, 90.2%, 91.4%, and 0.96 accuracy, sensitivity, specificity, precision, and AUC results were obtained, respectively. In the classification of rheumatoid arthritis, osteoarthritis, and normal hand radiographs, an 80.6% accuracy result was obtained. In this study, to develop an end-to-end computerized method, the YOLOv4 algorithm was used for object detection, and a pre-trained VGG-16 network was used for the classification of hand radiographs. This computer-aided diagnosis method can assist clinicians in interpreting hand radiographs, especially in rheumatoid arthritis and osteoarthritis.
      pubtype: Academic Journal
      doctype:
        diagnostic images
        equations & formulas
        research
        tables/charts
        Journal Article
      ougenre: Article
    language: English
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