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Safety margins in robotic bone milling: from registration uncertainty to statistically safe surgeries
Author(s) -
Siebold Michael A.,
Dillon Neal P.,
Fichera Loris,
Labadie Robert F.,
Webster Robert J.,
Fitzpatrick J. Michael
Publication year - 2017
Publication title -
the international journal of medical robotics and computer assisted surgery
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.556
H-Index - 53
eISSN - 1478-596X
pISSN - 1478-5951
DOI - 10.1002/rcs.1773
Subject(s) - context (archaeology) , computer science , patient safety , margin (machine learning) , image registration , accidental , variance (accounting) , computer vision , artificial intelligence , medicine , image (mathematics) , machine learning , acoustics , geology , paleontology , health care , business , economics , economic growth , physics , accounting
Background When robots mill bone near critical structures, safety margins are used to reduce the risk of accidental damage due to inaccurate registration. These margins are typically set heuristically with uniform thickness, which does not reflect the anisotropy and spatial variance of registration error. Methods A method is described to generate spatially varying safety margins around vital anatomy using statistical models of registration uncertainty. Numerical simulations are used to determine the margin geometry that matches a safety threshold specified by the surgeon. Results The algorithm was applied to CT scans of five temporal bones in the context of mastoidectomy, a common bone milling procedure in ear surgery that must approach vital nerves. Safety margins were generated that satisfied the specified safety levels in every case. Conclusions Patient safety in image‐guided surgery can be increased by incorporating statistical models of registration uncertainty in the generation of safety margins around vital anatomy.