Artificial intelligence-based analysis of associations between learning curve and clinical outcomes in endoscopic and microsurgical lumbar decompression surgery.
Babak SaraviAlisia ZinkSara ÜlkümenSebastien Couillard-DespresGernot LangFrank HasselPublished 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 (2023)
A median of 72 cases of FED surgeries led to comparable clinical outcomes in the early learning curve phase compared to experienced surgeons. These outcomes seem to be more significantly affected by patient characteristics than the learning curve or the surgical technique. Several study variables, including the learning curve, can be used to predict whether lumbar decompression surgery will result in an increased LOS, OT, or complications. To introduce the provided prediction tools into clinics, the algorithms need to be implemented into open-source software and externally validated through large-scale randomized controlled trials.
Keyphrases
- minimally invasive
- artificial intelligence
- machine learning
- deep learning
- big data
- randomized controlled trial
- primary care
- coronary artery bypass
- quality improvement
- risk factors
- systematic review
- type diabetes
- metabolic syndrome
- internal carotid artery
- acute coronary syndrome
- percutaneous coronary intervention
- study protocol
- meta analyses