Machine learning models for predicting tibial intramedullary nail length.

Journal: BMC musculoskeletal disorders
PMID: 40259255

Abstract

BACKGROUND: Tibial intramedullary nailing (IMN) represents a standard treatment for fractures of the tibial shaft. Nevertheless, accurately predicting the appropriate nail length prior to surgery remains a challenging endeavour. Conventional techniques frequently depend on data obtained intraoperatively, which may prolong surgical time and elevate radiation exposure. This study employs anthropometric measurements to evaluate and contrast the efficacy of machine learning (ML) models in predicting tibial IMN length.

Authors

  • Sercan Capkin
    Faculty of Medicine, Department of Orthopaedics and Traumatology, Izmir Bakircay University, Izmir, 36665, Turkey. sercancapkn@gmail.com.
  • Ali Ihsan Kilic
    Faculty of Medicine, Department of Orthopaedics and Traumatology, Izmir Bakircay University, Izmir, 36665, Turkey.
  • Hakan Cici
    Faculty of Medicine, Department of Orthopaedics and Traumatology, Izmir Democracy University, Izmir, Turkey.
  • Mehmet Akdemir
    Department of Orthopaedics and Traumatology, Izmir Ekol Hospital, Izmir, Turkey.
  • Mert Kahraman Marasli
    Department of Orthopaedics and Traumatology, Gebze Fatih State Hospital, Kocaeli, Turkey.

Keywords

External Resources

Popular Topics
Recent Journals