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PER-8 PROV
determining a contour for a bone-facing surface of the patient specific cutting guide model; and
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PER-8 PROV
2. The method of claim 1, wherein designing the patient specific cutting guide model further comprises:
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PER-8 PROV
manufacturing a patient specific cutting guide based on the patient specific cutting guide model.
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PER-8 PROV
designing a patient specific cutting guide model based on the template cutting guide model; and
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PER-8 PROV
registering the template cutting guide model with bones of the bone model;
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PER-8 PROV
selecting a template cutting guide model for an osteotomy procedure configured to correct the deformity;
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PER-8 PROV
determining a deformity between two bones of the patient’s foot based on the anatomic data;
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PER-8 PROV
determining anatomic data based on a bone model of the patient’s foot;
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PER-8 PROV
1.A method for generating one or more patient specific instruments configured to correct a condition present in a patient’s foot, the method comprising:
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PER-8 PROV
In another embodiment, the server 2404 may be configured to display footwear having standard sizes and once a user 2410 selects a set of shoes for purchase, the server 2404 may analyze the foot model 2402 in relation to the selected set of shoes and determine adjustments to make to the set of shoes such that the shoes will provide a custom fit for the user. These customizations may be provided to a shoemaker who may then custom make the purchased shoes which are then sent to the user 2412.
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PER-8 PROV
3. The method of claim 1, further comprising a user modifying the selected template cutting guide model to configure features of the modified template cutting guide model specific to a patient specific osteotomy procedure for the patient.
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PER-8 PROV
The server 2404 may use the foot model 2402 to determine what sizes, shapes, and styles of footwear for purchase to display on the web pages 2408 to the user 2412. The server 2404 may be configured to automatically select footwear for purchase to display. For example, in one embodiment, the server 2404 includes an artificial intelligence or machine learning module. The artificial intelligence or machine learning module may be configured to implement one or more of a variety of artificial intelligence modules that may be trained for selecting footwear for purchase to display based on anatomic data 1812 and/or other input parameters.
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PER-8 PROV
The exemplary system 2400 may include a foot model 2402, a server 2404, a network 2406, a web pages 2408, client devices 2410, and a user 2412. The foot model 2402 may be generated from one or more medical imaging images or data of a user’s foot or feet. The foot model 2402 may be unique to the user 2412.
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PER-8 PROV
Figure 24 illustrates an exemplary system 2400 configured to generate patient specific footwear configured to a specific patient’s feet, according to one embodiment. Those of skill in the art will appreciate that the technologies, concepts, and embodiments presented herein can be applied to other contexts to satisfy custom patient specific needs. For example, technologies for selecting a selected template cutting guide model 1834 can be applied to selection of a foot size, shape, and configuration for fitting to standard sized footwear or for manufacture of custom sized and shaped footwear.
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PER-8 PROV
The exports (1804, 1812, 2202, 2306, and 2308) may be inputs for a variety of 3rd party tools 2310 including a manufacturing tool, a simulation tool, a virtual reality tool, an augmented reality tool, an operative procedure simulation tool, a robotic assistance tool, and the like. A surgeon can then use these tools when performing an osteotomy procedure or for rehearsals and preparation for the osteotomy procedure. For example, a physical model of the bones, patient specific cutting guide 1806, and/or fixators can be fabricated, and these can be used for a rehearsal operative procedure. Alternatively, a surgeon can use the bone model 1804, selected template cutting guide model 1834, and/or a fixator model to perform a simulated osteotomy procedure using an operative procedure simulation tool.
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PER-8 PROV
The export module 2304 is configured to enable exporting of a patient specific cutting guide model 2202 for a variety of purposes including, but not limited to, fabrication/manufacture of a patient specific cutting guide 1806 and/or fixator(s), generation of a preoperative plan, generation of a physical bone model matching the bone model 1804, and the like. In one embodiment, the export module 2304 is configured to export the bone model 1804, anatomic data 1812, a patient specific cutting guide model 2202, a preoperative plan 2306, a fixator model 2308, or the like. In this manner the custom instrumentation and/or procedural steps for an osteotomy procedure can be used in other tools. The preoperative plan 2306 may include a set of step by step instructions or recommendation for a surgeon or other staff in performing an osteotomy procedure such as patient specific osteotomy procedure. The preoperative plan 2306 may include images and text instructions and may include identification of instrumentation to be used for different steps of the osteotomy procedure. The instrumentation may include the patient specific cutting guide 1806 and/or one or more fixators. In one embodiment, the export module 2304 may provide a fixator model which can be used to fabricate a fixator for the osteotomy procedure.
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PER-8 PROV
In one embodiment, the fixator selector 2302 includes an artificial intelligence or machine learning module. The artificial intelligence or machine learning module is configured to implement one or more of a variety of artificial intelligence modules that may be trained for selecting fixator(s) based on anatomic data 1812 and/or other input parameters. In one embodiment, the artificial intelligence or machine learning module may be trained using a large data set of anatomic data 1812 for suitable fixator(s) identified and labeled in the dataset by professionals for use to treat a particular deformity 1826. The artificial intelligence or machine learning module may implement, or use, a neural network configured according to the training such that as the artificial intelligence or machine learning module is able to select or recommend suitable fixator(s).
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PER-8 PROV
As used herein, a "fixator" refers to an apparatus, instrument, structure, device, component, member, system, assembly, or module structured, organized, configured, designed, arranged, or engineered to connect two bones or bone fragments or a single bone or bone fragment and another fixator to position and retain the bone or bone fragments in a desired position and/or orientation. Examples of fixators include both those for external fixation as well as those for internal fixation and include, but are not limited to pins, wires, Kirschner wires, screws, anchors, bone anchors, plates, bone plates, intramedullary nails or rods or pins, implants, interbody cages, fusion cages, and the like.
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PER-8 PROV
The fixator selector 2302 enables a user to determine which fixator(s) to use for an osteotomy procedure planned for a patient. In one embodiment, the fixator selector 2302 may recommend one or more fixators based on the bone model 1804, the deformity 1826 or input from a user or a history of prior osteotomy procedures performed to correct a particular deformity 1826. In one embodiment, the fixator selector 2302 selects a bone plate for fixation of two bones of the patient during an osteotomy procedure. The fixator selector 2302 may select a fixator model from a set of predefined fixator models or select a physical fixator from a set of fixators. The fixators may include a plate and associated accessories such as screws, anchors, and the like.
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PER-8 PROV
Figure 23 illustrates an exemplary system 2300 configured to generate one or more patient specific instruments configured to correct a bone condition, according to one embodiment. The system 2300 may include similar components or modules to those described in relation to FIG. 18. In addition, the system 2300 may include a fixator selector 2302 and/or an export module 2304.
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