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Deep learning-based classification of erosion, synovitis and osteitis in hand MRI of patients with inflammatory arthritis.
Schlereth, Maja; Mutlu, Melek Yalcin; Utz, Jonas; Bayat, Sara; Heimann, Tobias; Qiu, Jingna; Ehring, Chris; Liu, Chang; Uder, Michael; Kleyer, Arnd; Simon, David; Roemer, Frank; Schett, Georg; Breininger, Katharina; Fagni, Filippo.
Afiliación
  • Schlereth M; Department of Artificial Intelligence in Biomedical Engineering, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany maja.schlereth@fau.de.
  • Mutlu MY; Department of Internal Medicine 3-Rheumatology and Immunology, Friedrich-Alexander-University Erlangen-Nürnberg (FAU) and Universitätsklinikum Erlangen, Erlangen, Germany.
  • Utz J; Deutsches Zentrum Immuntherapie (DZI), Friedrich-Alexander-University Erlangen-Nürnberg (FAU) and Universitätsklinikum Erlangen, Erlangen, Germany.
  • Bayat S; Department of Artificial Intelligence in Biomedical Engineering, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany.
  • Heimann T; Department of Internal Medicine 3-Rheumatology and Immunology, Friedrich-Alexander-University Erlangen-Nürnberg (FAU) and Universitätsklinikum Erlangen, Erlangen, Germany.
  • Qiu J; Deutsches Zentrum Immuntherapie (DZI), Friedrich-Alexander-University Erlangen-Nürnberg (FAU) and Universitätsklinikum Erlangen, Erlangen, Germany.
  • Ehring C; Digital Technology and Innovation, Siemens Healthcare GmbH, Erlangen, Germany.
  • Liu C; Department of Artificial Intelligence in Biomedical Engineering, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany.
  • Uder M; Institute of Radiology, Friedrich-Alexander-University Erlangen-Nürnberg (FAU) and Universitätsklinikum Erlangen, Erlangen, Germany.
  • Kleyer A; Department of Computer Science, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany.
  • Simon D; Institute of Radiology, Friedrich-Alexander-University Erlangen-Nürnberg (FAU) and Universitätsklinikum Erlangen, Erlangen, Germany.
  • Roemer F; Department of Internal Medicine 3-Rheumatology and Immunology, Friedrich-Alexander-University Erlangen-Nürnberg (FAU) and Universitätsklinikum Erlangen, Erlangen, Germany.
  • Schett G; Deutsches Zentrum Immuntherapie (DZI), Friedrich-Alexander-University Erlangen-Nürnberg (FAU) and Universitätsklinikum Erlangen, Erlangen, Germany.
  • Breininger K; Department of Rheumatology and Clinical Immunology, Charité - Universitätsmedizin Berlin, Berlin, Germany.
  • Fagni F; Department of Internal Medicine 3-Rheumatology and Immunology, Friedrich-Alexander-University Erlangen-Nürnberg (FAU) and Universitätsklinikum Erlangen, Erlangen, Germany.
RMD Open ; 10(2)2024 Jun 17.
Article en En | MEDLINE | ID: mdl-38886001
ABSTRACT

OBJECTIVES:

To train, test and validate the performance of a convolutional neural network (CNN)-based approach for the automated assessment of bone erosions, osteitis and synovitis in hand MRI of patients with inflammatory arthritis.

METHODS:

Hand MRIs (coronal T1-weighted, T2-weighted fat-suppressed, T1-weighted fat-suppressed contrast-enhanced) of rheumatoid arthritis (RA) and psoriatic arthritis (PsA) patients from the rheumatology department of the Erlangen University Hospital were assessed by two expert rheumatologists using the Outcome Measures in Rheumatology-validated RA MRI Scoring System and PsA MRI Scoring System scores and were used to train, validate and test CNNs to automatically score erosions, osteitis and synovitis. Scoring performance was compared with human annotations in terms of macro-area under the receiver operating characteristic curve (AUC) and balanced accuracy using fivefold cross-validation. Validation was performed on an independent dataset of MRIs from a second patient cohort.

RESULTS:

In total, 211 MRIs from 112 patients (14 906 region of interests (ROIs)) were included for training/internal validation using cross-validation and 220 MRIs from 75 patients (11 040 ROIs) for external validation of the networks. The networks achieved high mean (SD) macro-AUC of 92%±1% for erosions, 91%±2% for osteitis and 85%±2% for synovitis. Compared with human annotation, CNNs achieved a high mean Spearman correlation for erosions (90±2%), osteitis (78±8%) and synovitis (69±7%), which remained consistent in the validation dataset.

CONCLUSIONS:

We developed a CNN-based automated scoring system that allowed a rapid grading of erosions, osteitis and synovitis with good diagnostic accuracy and using less MRI sequences compared with conventional scoring. This CNN-based approach may help develop standardised cost-efficient and time-efficient assessments of hand MRIs for patients with arthritis.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Osteítis / Sinovitis / Imagen por Resonancia Magnética / Aprendizaje Profundo Límite: Adult / Aged / Female / Humans / Male / Middle aged Idioma: En Revista: RMD Open Año: 2024 Tipo del documento: Article País de afiliación: Alemania Pais de publicación: Reino Unido

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Osteítis / Sinovitis / Imagen por Resonancia Magnética / Aprendizaje Profundo Límite: Adult / Aged / Female / Humans / Male / Middle aged Idioma: En Revista: RMD Open Año: 2024 Tipo del documento: Article País de afiliación: Alemania Pais de publicación: Reino Unido