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Assessment of inflammation in patients with rheumatoid arthritis using thermography and machine learning: a fast and automated technique.
Morales-Ivorra, Isabel; Narváez, Javier; Gómez-Vaquero, Carmen; Moragues, Carmen; Nolla, Joan M; Narváez, José A; Marín-López, Manuel Alejandro.
Afiliación
  • Morales-Ivorra I; Rheumatology Department, Hospital Universitari d'Igualada, Igualada, Spain isabel.morales.ivorra@gmail.com.
  • Narváez J; Rheumatology Department, Hospital Universitari de Bellvitge, L'Hospitalet de Llobregat, Spain.
  • Gómez-Vaquero C; Rheumatology Department, Hospital Universitari de Bellvitge, L'Hospitalet de Llobregat, Spain.
  • Moragues C; Rheumatology Department, Hospital Universitari de Bellvitge, L'Hospitalet de Llobregat, Spain.
  • Nolla JM; Rheumatology Department, Hospital Universitari de Bellvitge, L'Hospitalet de Llobregat, Spain.
  • Narváez JA; Radiodiagnosis Department, Hospital Universitari de Bellvitge, L'Hospitalet de Llobregat, Spain.
  • Marín-López MA; R&D Department, Singularity Biomed, Sant Cugat del Vallès, Spain.
RMD Open ; 8(2)2022 07.
Article en En | MEDLINE | ID: mdl-35840312
OBJECTIVES: Sensitive detection of joint inflammation in rheumatoid arthritis (RA) is crucial to the success of the treat-to-target strategy. In this study, we characterise a novel machine learning-based computational method to automatically assess joint inflammation in RA using thermography of the hands, a fast and non-invasive imaging technique. METHODS: We recruited 595 patients with arthritis and osteoarthritis, as well as healthy subjects at two hospitals over 4 years. Machine learning was used to assess joint inflammation from the thermal images of the hands using ultrasound as the reference standard, obtaining a Thermographic Joint Inflammation Score (ThermoJIS). The machine learning model was trained and tuned using data from 449 participants with different types of arthritis, osteoarthritis or without rheumatic disease (development set). The performance of the method was evaluated based on 146 patients with RA (validation set) using Spearman's rank correlation coefficient, area under the receiver-operating curve (AUROC), average precision, sensitivity, specificity, positive and negative predictive value and F1-score. RESULTS: ThermoJIS correlated moderately with ultrasound scores (grey-scale synovial hypertrophy=0.49, p<0.001; and power Doppler=0.51, p<0.001). The AUROC for ThermoJIS for detecting active synovitis was 0.78 (95% CI, 0.71 to 0.86; p<0.001). In patients with RA in clinical remission, ThermoJIS values were significantly higher when active synovitis was detected by ultrasound. CONCLUSIONS: ThermoJIS was able to detect joint inflammation in patients with RA, even in those in clinical remission. These results open an opportunity to develop new tools for routine detection of joint inflammation.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Osteoartritis / Artritis Reumatoide / Sinovitis Tipo de estudio: Diagnostic_studies / Etiology_studies / Prognostic_studies Límite: Humans Idioma: En Revista: RMD Open Año: 2022 Tipo del documento: Article País de afiliación: España Pais de publicación: Reino Unido
Texto completo
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Osteoartritis / Artritis Reumatoide / Sinovitis Tipo de estudio: Diagnostic_studies / Etiology_studies / Prognostic_studies Límite: Humans Idioma: En Revista: RMD Open Año: 2022 Tipo del documento: Article País de afiliación: España Pais de publicación: Reino Unido