Sugano Nobuhiko (ed.) Computer Assisted Orthopaedic Surgery for Hip and Knee: Current State of the Art in Clinical Application and Basic Research

Springer, 2018. — 487 p.This book focuses on two major areas in the field of computer assisted orthopaedic surgery (CAOS): hip and knee surgery. It reviews the current clinical status of the various CAOS tools for hip and knee arthroplasty, osteotomy, ligament reconstruction, spine surgery, trauma surgery, and tumour surgery that have become available in recent years and discusses future applications based on fundamental research and continuously developing computer technology / devices. Computer Assisted Orthopaedic Surgery for Hip and Knee highlights three areas – total knee arthroplasty (TKA); total hip arthroplasty (THA) and hip osteotomy; and statistical shape modeling. It is a valuable resource for orthopaedic surgeons, clinical technologists and computer scientists and other specialists interested in this technology.Computet-Assisted TKA
Navigation of Alignment and Balancing During Knee Replacement
CT-Based Navigation for Total Knee Arthroplasty
Robotic Total Knee Arthroplasty
Patient-Specific Templates for Total Knee Arthroplasty
UKA Computer Navigation
Robotic UKA
Computer-Assisted THA, Hip Osteotomy, and Tumor Surgery
Pelvic and Femoral Coordinates and Implant Alignment Representations in THA
Computed Tomography-Based Navigation for Total Hip Arthroplasty
Imageless Computer-Assisted Navigation for Total Hip Arthroplasty
Patient-Specific Surgical Guide for Total Hip Arthroplasty
Robotic Primary and Revision THA for the Femoral Side
Computer-Assisted Orthopedic Surgery for Hip Osteotomy
CAOS in Bone Tumor Surgery
Statistical Shape Modeling for Computer-Assisted Hip and Knee Surgery
Application of Statistical Shape Modeling for CAOS:​ A Tutorial
Statistical Shape Models and Atlases:​ Application to 2D-3D Reconstruction in THA
Construction and Application of Large-Scale Image Database in Orthopedic Surgery
Future Perspectives on Statistical Shape Models in Computer-Aided Orthopedic Surgery:​ Beyond Statistical Shape Models and on to Big Data