Estimating lumbar bone mineral density from conventional MRI and radiographs with deep learning in spine patients.
Luca Maria SconfienzaAndrea CinaDave O'RiordanJacopo Antonino VitaleMarkus LoiblTamas F FeketeFrank S KleinstückDaniel HaschtmannAnne F MannionPublished in: European spine journal : official publication of the European Spine Society, the European Spinal Deformity Society, and the European Section of the Cervical Spine Research Society (2024)
The models showed good discriminative performances in detecting cases of low bone mineral density, and more limited capabilities for the direct estimation of its value. Being based on routine imaging and readily available data, such models are promising tools to retrospectively analyse existing datasets as well as for the opportunistic investigation of bone disorders.
Keyphrases
- bone mineral density
- postmenopausal women
- body composition
- deep learning
- end stage renal disease
- ejection fraction
- newly diagnosed
- magnetic resonance imaging
- chronic kidney disease
- high resolution
- prognostic factors
- minimally invasive
- electronic health record
- computed tomography
- photodynamic therapy
- mass spectrometry
- patient reported
- fluorescence imaging