Automated Diagnosis of Rheumatoid Arthritis From Hand Radiographs Using Artificial Intelligence: A Retrospective Study.
Background: Rheumatoid arthritis (RA) is a chronic inflammatory disease that damages hand and wrist joints, leading to pain, disability, and reduced quality of life. Radiographic assessment plays a key role in diagnosis, but it is subjective and dependent on the clinician's experience. Deep learning...
| Publicado en: | Clinical Medicine Insights: Arthritis & Musculoskeletal Disorders Vol. 19; pp. 1 - 8 |
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| Autores principales: | , , , |
| Formato: | diagnostic images research tables/charts Journal Article |
| Publicado: |
Sage Publications Inc.
03/06/2026
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=192153935&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 192153935 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 11795441 B3KJ jtl: Clinical Medicine Insights: Arthritis & Musculoskeletal Disorders issn: 11795441 maglogo: Y pubinfo: dt: 03/06/2026 vid: 19 pid: 20732 pub: Sage Publications Inc. place: Thousand Oaks, California artinfo: ui: 192153935 192153935 192153935 10.1177/11795441261429110 192153935 ppf: 1 ppct: 7 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Automated Diagnosis of Rheumatoid Arthritis From Hand Radiographs Using Artificial Intelligence: A Retrospective Study. aug: au: Çetintaş, Dilber Kılıçarslan, Gülhan Tuncer, Türkan Çetintaş, Derya affil: Department of Computer Engineering, Faculty of Engineering and Natural Sciences, University of Malatya Turgut Özal, Malatya, Türkiye sug: subj: Arthritis, Rheumatoid Radiography Diagnosis, Computer Assisted Hand Radiography Artificial Intelligence Utilization Human Male Female Retrospective Design Record Review Nonexperimental Studies Deep Learning Methods Wrist Joint Radiography Sensitivity and Specificity Quality of Life Radiologists Psychosocial Factors Pain Evaluation Image Interpretation, Computer Assisted Methods Decision Support Systems, Clinical Hospitals Descriptive Statistics Comparative Studies kappa Statistic Correlation Coefficient ROC Curve Male Female ab: Background: Rheumatoid arthritis (RA) is a chronic inflammatory disease that damages hand and wrist joints, leading to pain, disability, and reduced quality of life. Radiographic assessment plays a key role in diagnosis, but it is subjective and dependent on the clinician's experience. Deep learning–based systems offer the potential for faster, more objective, and more consistent evaluation. Objectives: This study aims to develop an attention-based deep learning model for the automated diagnosis of RA from hand and wrist radiographs and to demonstrate that high diagnostic performance can be achieved even with a limited data set. Design: Retrospective observational study evaluating an attention-based deep learning model for automated diagnosis of RA from hand and wrist radiographs. Methods: Radiographs from 311 RA patients and 259 healthy controls collected between September 2018 and September 2024 were analyzed. Individuals with other conditions causing hand deformities were excluded. The data set was divided into training (n = 325), validation (n = 142), and test (n = 50) sets. DenseNet121 and DenseNet169 architectures were combined with an attention mechanism to highlight RA-specific structural changes. Despite the relatively small data set, data augmentation and attention modeling were used to improve robustness. Results: The proposed model achieved 88% accuracy, 84% precision, and 91% recall, demonstrating strong diagnostic capability with limited training data. Initial clinical testing suggests that the model can support radiologists by providing consistent and objective assessments. Conclusion: This attention-based deep learning approach shows promise as an effective, reliable, and efficient tool for the automated diagnosis of RA. The ability to achieve high performance with limited data highlights its potential for real-world clinical adoption, particularly in resource-constrained environments. pubtype: Academic Journal doctype: diagnostic images research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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