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PER-12
Figure 12 illustrates an exemplary system 1200 configured to generate one or more patient specific instruments configured to correct a bone condition, according to one embodiment. "Bone condition" refers to any of a variety of conditions of bones of a patient. Bone conditions may be caused by or result from deformities, misalignment, malrotation, fractures, joint failure, and/or the like. A bone condition includes, but is not limited to, any angular deformities of one or more bone segments in either the lower or upper extremities (for example, tibial deformities, calcaneal deformities, femoral deformities, and radial deformities). "Malrotation" refers to a condition in which a part, typically a part of a patient's body has rotated from a normal position to an unnormal or uncommon position. The system 1200 may include an apparatus 1202 configured to accept, review, receive or reference a bone model 1204 and provide a patient-specific cutting guide 1206. In one embodiment, the apparatus 1202 is a computing device. In another embodiment, the apparatus 1202 may be a combination of computing devices and/or software components or a single software component such as a software application.
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PER-12
The apparatus 1202 may include a determination module 1210, a deformity module 1220, a provision module 1230, a registration module 1240, a design module 1250, and a manufacturing module 1260. Each of which may be implemented in one or more of software, hardware, or a combination of hardware and software.
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PER-12
The determination module 1210 determines anatomic data 1212 from a bone model 1204. In certain embodiments, the system 1200 may not include a determination module 1210 if the anatomic data is available directly from the bone model 1204. In certain embodiments, the anatomic data for a bone model 1204 may include data that identifies each anatomic structure within the bone model 1204 and attributes about the anatomic structure. For example, the anatomic data may include measurements of the length, width, height, and density of each bone in the bone model. Furthermore, the anatomic data may include position information that identifies where each structure, such as a bone is in the bone model 1204 relative to other structures, including bones. The anatomic data may be in any suitable format and may be stored separately or together with data that defines the bone model 1204.
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PER-12
In one embodiment, the determination module 1210 may use advanced computer analysis system such as image segmentation to determine the anatomic data. Alternatively, or in addition the determination module 1210 may use software and/or systems that implement one or more artificial intelligence methods (e.g., machine learning and/or neural networks) for deriving, determining, or extrapolating, anatomic data from the bone model. In one embodiment, the determination module 1210 may perform an anatomic mapping of the bone model 1204 to determine each unique aspect of the intended osteotomy procedure and/or bone resection and/or bone translation. The anatomic mapping may be used to determine coordinates to be used for an osteotomy procedure, position and manner of resections to be performed either manually or automatically or using robotic surgical assistance, a width for bone cuts, an angle for bone cuts, a predetermined depth for bone cuts, dimensions and configurations for resection instruments such as saw blades, milling bit size and/or speed, saw blade depth markers, and/or instructions for automatic or robotic resection operations.
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PER-12
The deformity module 1220 determines or identifies one or more deformities or other anomalies based on the anatomic data 1212. The deformity may include a deformity between two bones of a patient’s foot as represented in the bone model 1204. In one embodiment, the deformity module 1220 may compare the anatomic data 1212 to a general model that is representative of most patient’s anatomies and that does not have a deformity or anomaly. In one embodiment, if the anatomic data 1212 does not match the general model a deformity is determined. Various deformities may be detected including those that have well-known names for the condition and those that are unnamed.
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PER-12
The provision module 1230 is configured to provide a preliminary cutting guide model 1238. The provision module 1230 may use a variety of methods to provide the preliminary cutting guide model. In one embodiment, the provision module 1230 may generate preliminary cutting guide model. In the same or an alternative embodiment, the provision module 1230 may select a template cutting guide model for an osteotomy procedure configured to correct the deformity identified by the deformity module 1220. In one embodiment, the provision module 1230 may select a template cutting guide model from a set of template cutting guide models (e.g., a library, set, or repository of template cutting guide models).
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PER-12
The registration module 1240 registers the preliminary cutting guide model with one or more bones or other anatomical structures of the bone model 1204. As explained above, registration is a process of combining medical imaging data, patient imaging data, and/or one or more models such that the preliminary cutting guide model can be used with the bone model 1204.
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PER-12
The design module 1250 designs a patient-specific cutting guide (or patient-specific cutting guide model) based on the preliminary cutting guide model. The design operation of the design module 1250 may be completely automated, partially automated, or completely manual. A user may control how automated or manual the designing of the patient-specific cutting guide (or patient-specific cutting guide model) is.
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PER-12
The manufacturing module 1260 may manufacture a patient-specific cutting guide 1206 using the preliminary cutting guide model. The manufacturing module 1260 may use a patient-specific cutting guide model generated from the preliminary cutting guide model. The manufacturing module 1260 may provide the patient-specific cutting guide model to one or more manufacturing tools and/or fabrication tool. The patient-specific cutting guide model may be sent to the tools in any format such as an STL file or any other CAD modeling or CAM file or method for data exchange. In one embodiment, a user can adjust default parameters for the patient-specific cutting guide such as types and/or thicknesses of materials, dimensions, and the like before the manufacturing module 1260 provides the patient-specific cutting guide model to a manufacturing tool.
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PER-12
Effective connection of the cutting guide to one or more bones across a joint can ensure that cut surfaces are formed in desired locations and orientation and mitigate removal of hard tissue and/or soft tissue outside in undesired locations.
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PER-12
Figure 13 illustrates an exemplary deformity module 1220 configured to determine a deformity, according to one embodiment. The deformity module 1220 may detect one or more deformities and/or anomalies of a patient’s anatomy by analyzing anatomic data 1212 and other inputs, such as a certain type or class of deformities to search for.
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PER-12
The deformity module 1220 may be completely automated, partially automated, or completely manual. A user may control how automated or manual the detection of the deformity is. The user may provide instructions to the deformity module 1220 to facilitate automatic or partially automated detection or determination of one or more deformities.
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PER-12
The deformity module 1220 may include a deformity detection module 1222. The deformity detection module 1222 may be configured for automated determination of a deformity. For example, in one embodiment, the deformity detection module 1222 includes an artificial intelligence or machine learning module 1224. The artificial intelligence or machine learning module 1224 is configured to implement one or more of a variety of artificial intelligence modules that may be trained for detecting an anomaly or deformity based on anatomic data 1212. In another embodiment, the deformity module 1220 may receive patient imaging data, a bone model, a CAD model or the like and use these inputs to determine deformities in the bones of a patient.
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PER-12
In one embodiment, the artificial intelligence or machine learning module 1224 may be trained using a large data set of anatomic data 1212 for healthy non-deformed bones and a large data set of anatomic data 1212 for deformed bones in which the deformity has been previously identified and labeled in the dataset. The artificial intelligence or machine learning module 1224 may implement, or use, a neural network configured according to the training such that as the artificial intelligence or machine learning module 1224 accepts the anatomic data 1212 for a particular patient, the artificial intelligence or machine learning module 1224 is able to determine what deformity 1226 exists in the patient’s bones, when such a deformity 1226 exists.
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PER-12
Figures 14A and 14B illustrates one example of a method for determining a deformity and a correction for the deformity, according to one embodiment. The deformity module 1220 may use a particular method for determining whether or not two or more bones have a deformity 1226. The deformity module 1220 may use one or more advanced computing techniques for determining the deformity 1226.
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PER-12
Referring now to Figure 14A, in one embodiment, the deformity module 1220 starts by identifying a center longitudinal axis 1228a, 1228b (Fig. 14A shows two of the plurality of axes for clarity) for each bone in the bone model 1204. For example, the deformity module 1220 may identify the center longitudinal axis 1228a for a first metatarsal and the center longitudinal axis 1228b for the second metatarsal.
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PER-12
Next, the deformity module 1220 may identify a reference axis 1229 perpendicular with one of the center longitudinal axes 1228a,b, such as center longitudinal axis 1228b. The reference axis 1229 may be at or near a joint between bones of the bone model 1204. The deformity module 1220 may determine that a deformity 1226 exists if the center longitudinal axes 1228a,b are not parallel or are not parallel when measured with a predefined margin for error. Figure 13A illustrates a bone model 1204 with a deformity 1226. The deformity 1226 is that the first metatarsal is not parallel or not sufficiently parallel to the second metatarsal at the joint between the first metatarsal and the medial cuneiform bones. Once the deformity 1226 is determined, the deformity module 1220 or apparatus 1202 may determine what steps, procedures, or instrumentation can be used to correct the deformity 1226. The deformity module 1220 may use a name, label, tag, or other identifier for a particular deformity 1226.
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PER-12
Figure 14B illustrates the bone model 1204 of Figure 14A after a corrective procedure and/or application of corrective implants may be performed. The center longitudinal axes 1228a,b are parallel, or sufficiently parallel, such that the deformity 1226 is not a problem for a patient.
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PER-12
Figure 15 illustrates an exemplary provision module 1230 configured to provide a preliminary cutting guide model, according to one embodiment. The provision module 1230 may accept anatomic data 1212 and a designation, identifier, label, or name of a deformity 1226. In the illustrated embodiment, the provision module 1230 may generate a preliminary cutting guide model 1238 (e.g., generate from ‘scratch’) or the provision module 1230 may select a template cutting guide model 1236 automatically from a set of template cutting guide models 1236 stored in a repository 1502. The provision module 1230 may incorporate a variety of parameters in order to provision, generate, determine, or select a template cutting guide model 1236. For example, in addition to the anatomic data 1212, the provision module 1230 may include patient imaging data, deformity parameters for a variety of angular deformities (in all 3 planes) of the midfoot or hind foot and ankle where an osteotomy could be used, patient preferences, and/or surgeon input parameters.
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PER-12
In one embodiment, the provision module 1230 may include a generator 1232 and/or a selection module 1234. In one embodiment, the generator 1232 is configured to generate a preliminary cutting guide model 1238. In certain embodiments, the generator 1232 may generate or create the preliminary cutting guide model based on anatomic data and/or a bone model or a combination of these and no other inputs. (e.g. no model or predesigned structure, template, or prototype). Alternatively, or in addition, the generator 1232 may generate or create the preliminary cutting guide model using a standard set of features or components that can be combined to form the preliminary cutting guide model. The generated preliminary cutting guide model may subsequently be modified or revised by an automated process, and/or manual process, to generate the preliminary cutting guide model used in this disclosure.
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