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Generative Anatomy Modeling Language (GAML)
Author(s) -
Demirel Doga,
Yu Alexander,
BaerCooper Seth,
Halic Tansel,
Bayrak Coskun
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.1813
Subject(s) - computer science , generative grammar , generative model , natural language processing , artificial intelligence , anatomy , medicine
Background This paper presents the Generative Anatomy Modeling Language (GAML) for generating variation of 3D virtual human anatomy in real‐time. This framework provides a set of operators for modification of a reference base 3D anatomy. The perturbation of the 3D models is satisfied with nonlinear geometry constraints to create an authentic human anatomy. Methods GAML was used to create 3D difficult anatomical scenarios for virtual simulation of airway management techniques such as Endotracheal Intubation (ETI) and Cricothyroidotomy (CCT). Difficult scenarios for each technique were defined and the model variations procedurally created with GAML. Conclusion This study presents details of the GAML design, set of operators, types of constraints. Cases of CCT and ETI difficulty were generated and confirmed by expert surgeons. Execution performance pertaining to an increasing complexity of constraints using nonlinear programming was in real‐time execution.

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