Publications and Projects

Current Projects

  • Automated intraoperative anatomical structure recognition
  • Fast CT to MRI synthesis in the lumbar spine
  • Prediction of functional impairment after intracranial tumor surgery
  • Predictive analytics in pituitary surgery
  • Low-level entry course on machine learning-based predictive analytics for clinicians


Staartjes VE, Stienen MN (2019) Data Mining in Spine Surgery: Leveraging Electronic Health Records for Machine Learning and Clinical Research. Neurospine 16 (4), 654-656.
doi: 10.14245/ns.1938434.217

Quddusi A, Eversdijk HAJ, Klukowska AM, de Wispelaere MP, Kernbach JM, Schröder ML, Staartjes VE (2019) External validation of a prediction model for pain and functional outcome after elective lumbar spinal fusion. European Spine Journal.
doi: 10.1007/s00586-019-06189-6

Staartjes VE, Zattra CM, Akeret K, Maldaner N, Muscas G, Bas van Niftrik CH, Fierstra J, Regli L, Serra C (2019) Neural network-based identification of patients at high risk for intraoperative cerebrospinal fluid leaks in endoscopic pituitary surgery. Journal of Neurosurgery 1–7.
doi: 10.3171/2019.4.JNS19477

van Niftrik CHB, van der Wouden F, Staartjes VE, Fierstra J, Stienen MN, Akeret K, Sebök M, Fedele T, Sarnthein J, Bozinov O, Krayenbühl N, Regli L, Serra C (2019) Machine Learning Algorithm Identifies Patients at High Risk for Early Complications After Intracranial Tumor Surgery: Registry-Based Cohort Study. Neurosurgery
doi: 10.1093/neuros/nyz145

Siccoli A, de Wispelaere MP, Schröder ML, Staartjes VE (2019) Machine learning-based preoperative predictive analytics for lumbar spinal stenosis. Neurosurgical Focus 46:E5.
doi: 10.3171/2019.2.FOCUS18723

Staartjes VE, de Wispelaere MP, Vandertop WP, Schröder ML (2019) Deep learning-based preoperative predictive analytics for patient-reported outcomes following lumbar discectomy: feasibility of center-specific modeling. The Spine Journal 19:853–861.
doi: 10.1016/j.spinee.2018.11.009

Staartjes VE, Serra C, Muscas G, Maldaner N, Akeret K, van Niftrik CHB, Fierstra J, Holzmann D, Regli L (2018) Utility of deep neural networks in predicting gross-total resection after transsphenoidal surgery for pituitary adenoma: a pilot study. Neurosurgical Focus 45:E12.
doi: 10.3171/2018.8.FOCUS18243

Staartjes VE, Schröder ML (2018) Class imbalance in machine learning for neurosurgical outcome prediction: are our models valid? Journal of Neurosurgery: Spine 29:611–612.
doi: 10.3171/2018.5.SPINE18543