Building and Testing a Statistical Shape Model of the Human Ear Canal
MICCAI '02 Proceedings of the 5th International Conference on Medical Image Computing and Computer-Assisted Intervention-Part II
Expert Systems: Principles and Programming
Expert Systems: Principles and Programming
Rapid Manufacturing: An Industrial Revolution for the Digital Age
Rapid Manufacturing: An Industrial Revolution for the Digital Age
Proceedings of the 2008 ACM symposium on Solid and physical modeling
Automatic recognition of features from freeform surface CAD models
Computer-Aided Design
Reverse innovative design - an integrated product design methodology
Computer-Aided Design
Customized Design of Hearing Aids Using Statistical Shape Learning
MICCAI '08 Proceedings of the 11th international conference on Medical Image Computing and Computer-Assisted Intervention - Part I
Analysis of deformation of the human ear and canal caused by mandibular movement
MICCAI'07 Proceedings of the 10th international conference on Medical image computing and computer-assisted intervention
Automatic detection of anatomical features on 3D ear impressions for canonical representation
MICCAI'10 Proceedings of the 13th international conference on Medical image computing and computer-assisted intervention: Part III
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In the manufacturing of customized medical prostheses, such as in-the-ear hearing aids, the design process often is dictated by a source template representing the anatomy of a patient and a set of work instructions representing the description of surface modifications. Instead of carrying out the work instructions by hand with knife, file or drilling tools, the state-of-the-art relies on modern software tools, such as computer-aided-design and computer-aided-manufacturing. Work instructions are usually defined in terms of anatomical landmarks of a given template. Following the design phase, the virtual model of the customized prosthesis is produced by a rapid prototyping system, like selective laser sintering or stereolithography. An outstanding problem in prostheses design is that the work instructions are often vaguely defined, and a suitable outcome largely depends on the knowledge, experience and skill of the designer. In this paper, we present a solution to minimize the influence of human interaction. Our approach involves the abstraction of the work instructions into expert system rules that exploit a robustly identified canonical set of anatomic features. The versatility of our approach lies in a priori defining an entire design workflow through a rule set, thereby yielding a high degree of automation that is flexible, customizable, consistent, and reproducible. The proposed solution is extensively evaluated in a real world application, and is shown to yield significant improvement in manufacturing. For instance, the consistency of the outcome was improved by about 10% and the design time was reduced by about 8.4%.