Evidence- and data-driven classification of low back pain via artificial intelligence: Protocol of the PREDICT-LBP study

In patients presenting with low back pain (LBP), once specific causes are excluded (fracture, infection, inflammatory arthritis, cancer, cauda equina and radiculopathy) many clinicians pose a diagnosis of non-specific LBP. Accordingly, current management of non-specific LBP is generic. There is a ne...

Ausführliche Beschreibung

Gespeichert in:

Bibliographische Detailangaben
Veröffentlicht in:	PloS one 2023-08, Vol.18 (8), p.e0282346-e0282346
Hauptverfasser:	Belavy, Daniel L, Tagliaferri, Scott D, Tegenthoff, Martin, Enax-Krumova, Elena, Schlaffke, Lara, Bühring, Björn, Schulte, Tobias L, Schmidt, Sein, Wilke, Hans-Joachim, Angelova, Maia, Trudel, Guy, Ehrenbrusthoff, Katja, Fitzgibbon, Bernadette, Van Oosterwijck, Jessica, Miller, Clint T, Owen, Patrick J, Bowe, Steven, Döding, Rebekka, Kaczorowski, Svenja
Format:	Artikel
Sprache:	eng
Schlagworte:	Analysis Artificial intelligence Back pain Backache Biology and Life Sciences Cancer therapies Care and treatment Central nervous system Classification Clinical trials Clustering Composition Consent Diagnosis Disease Economic impact Health aspects Infections Inflammation Intervertebral discs Low back pain Machine learning Magnetic resonance Magnetic resonance imaging Medical research Medicine and Health Sciences Medicine, Experimental Nervous system Neural networks Neuroimaging Pain Patient outcomes Patients Research and Analysis Methods Sensitivity analysis Spinal cord Spinal stenosis Structure-function relationships Study Protocol Subgroups Surgery Systematic review Vertebrae
Online-Zugang:	Volltext
Tags:	Tag hinzufügen Keine Tags, Fügen Sie den ersten Tag hinzu!

Schreiben Sie den ersten Kommentar!