A machine learning pipeline for predicting bone marrow oedema along the sacroiliac joints on magnetic resonance imaging.
Joris RoelsAnn-Sophie De CraemerThomas RensonManouk de HoogeArne GevaertThomas Van Den BergheLennart JansNele HerregodsPhilippe CarronFilip Van den BoschYvan SaeysDirk ElewautPublished in: Arthritis & rheumatology (Hoboken, N.J.) (2023)
We propose a fully automated ML pipeline that enables objective and standardized evaluation of BMO along the SI joints on MRI. This method has the potential to screen large numbers of (suspected) SpA patients and is a step closer towards artificial intelligence assisted diagnosis and follow-up.
Keyphrases
- artificial intelligence
- machine learning
- magnetic resonance imaging
- deep learning
- bone marrow
- big data
- end stage renal disease
- ejection fraction
- chronic kidney disease
- high throughput
- newly diagnosed
- contrast enhanced
- mesenchymal stem cells
- computed tomography
- peritoneal dialysis
- pulmonary embolism
- prognostic factors
- patient reported outcomes
- human health
- climate change
- single cell