A deep learning model for the diagnosis of sacroiliitis according to Assessment of SpondyloArthritis International Society classification criteria with magnetic resonance imaging - 23/06/23
, Théodore Aouad c, Clementina Lopez Medina g, Sisi Yang b, d, Anna Molto e, f, Hugues Talbot c, Maxime Dougados d, e, f, Antoine Feydy b, d, f
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Highlights |
• | An artificial intelligence model was developed to detect sacroiliac bone marrow edema on MRI in patients with chronic inflammatory back pain. |
• | This artificial intelligence model determines the status of sacroiliac joint according to the assessment of spondyloarthritis international society (ASAS) classification, yielding 100% specificity but 56% sensitivity on external evaluation dataset. |
• | A good agreement between this artificial intelligence model and the majority decision of expert readers is obtained on the external evaluation dataset for the diagnosis of active sacroiliitis according to ASAS classification. |
Abstract |
Purpose |
The purpose of this study was to develop and evaluate a deep learning model to detect bone marrow edema (BME) in sacroiliac joints and predict the MRI Assessment of SpondyloArthritis International Society (ASAS) definition of active sacroiliitis in patients with chronic inflammatory back pain.
Materials and methods |
MRI examinations of patients from the French prospective multicenter DESIR cohort (DEvenir des Spondyloarthropathies Indifférenciées Récentes) were used for training, validation and testing. Patients with inflammatory back pain lasting three months to three years were recruited. Test datasets were from MRI follow-ups at five years and ten years. The model was evaluated using an external test dataset from the ASAS cohort. A neuronal network classifier (mask-RCNN) was trained and evaluated for sacroiliac joints detection and BME classification. Diagnostic capabilities of the model to predict ASAS MRI active sacroiliitis (BME in at least two half-slices) were assessed using Matthews correlation coefficient (MCC), sensitivity, specificity, accuracy and AUC. The gold standard was experts' majority decision.
Results |
A total of 256 patients with 362 MRI examinations from the DESIR cohort were included, with 27% meeting the ASAS definition for experts. A total of 178 MRI examinations were used for the training set, 25 for the validation set and 159 for the evaluation set. MCCs for DESIR baseline, 5-years, and 10-years follow-up were 0.90 (n = 53), 0.64 (n = 70), and 0.61 (n = 36), respectively. AUCs for predicting ASAS MRI were 0.98 (95% CI: 0.93–1), 0.90 (95% CI: 0.79–1), and 0.80 (95% CI: 0.62–1), respectively. The ASAS external validation cohort included 47 patients (mean age 36 ± 10 [SD] years; women, 51%) with 19% meeting the ASAS definition. MCC was 0.62, sensitivity 56% (95% CI: 42–70), specificity 100% (95% CI: 100–100) and AUC 0.76 (95% CI: 0.57–0.95).
Conclusion |
The deep learning model achieves performance close to those of experts for BME detection in sacroiliac joints and determination of active sacroiliitis according to the ASAS definition.
Le texte complet de cet article est disponible en PDF.Keywords : Artificial intelligence, Deep-learning, Magnetic resonance imaging, Sacroiliac joint, Spondyloarthritis
Abbreviations : AI, ASAS, BME, CI, CLAHE, DESIR, GESPIC, MCC, OMERACT, R-CNN, ROC, ROI, SpA, STIR
Plan
Vol 104 - N° 7-8
P. 373-383 - juillet 2023 Retour au numéro
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