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Machine learning techniques for computer-aided classification of active inflammatory sacroiliitis in magnetic
Matheus Calil Faleiros1, Marcello Henrique Nogueira-Barbosa2,3,4,5, Vitor Faeda Dalto6
1São Carlos School of Engineering, University of São Paulo, São Carlos, SP, Brazil.
Advances in Rheumatology (London, England)
|May 9, 2020
Summary
Machine learning, specifically the Multilayer Perceptron (MLP), shows promise in detecting active inflammatory sacroiliitis in axial spondyloarthritis (axSpA) patients using MRI scans, potentially improving diagnostic accuracy.
Area of Science:
- Medical Imaging
- Machine Learning in Healthcare
- Rheumatology
Background:
- Current diagnosis of active inflammatory sacroiliitis in axial spondyloarthritis (axSpA) relies on MRI, but interpretation by experts shows significant variability.
- This variability necessitates the exploration of objective diagnostic tools to improve consistency and accuracy in axSpA assessment.
Purpose of the Study:
- To investigate the efficacy of machine learning algorithms in identifying active inflammatory sacroiliitis on MRI in patients with axSpA.
- To assess the performance of different machine learning classifiers in detecting sacroiliac joint (SIJ) inflammation.
Main Methods:
- A retrospective analysis of 56 sacroiliac joint MRI exams was conducted, with data split into training (80%) and external test (20%) sets.
- Manual segmentation and feature extraction were performed, followed by classification using Support Vector Machine, Multilayer Perceptron (MLP), and Instance-Based Algorithm.
- Feature selection was enhanced using Relief and Wrapper methods, with performance evaluated via 10-fold cross-validation and external validation.
Main Results:
- The Multilayer Perceptron (MLP) classifier, using 6 features selected by the Wrapper method, achieved 100% sensitivity, 95.6% specificity, and 84.7% accuracy on the training dataset.
- On the external test dataset, the MLP classifier demonstrated 100% sensitivity, 66.7% specificity, and 80% accuracy.
- These results indicate strong performance of the MLP model in identifying sacroiliac joint subchondral bone marrow edema.
Conclusions:
- Machine learning methods, particularly the Multilayer Perceptron (MLP), show significant potential for aiding in the detection of active inflammatory sacroiliitis on MRI STIR sequences in axSpA patients.
- The developed MLP model offers a promising tool to enhance diagnostic objectivity and accuracy for sacroiliitis in axSpA.
Keywords:
Artificial intelligenceComputer-assisted diagnosisMachine learningMagnetic resonance imagingSacroiliac joint inflammationSpondyloarthritis
