Systems and methods for orthopedic implants
A computer-implemented method for designing a patient-specific orthopedic implant can include creating a user account associated with a patient. A patient-specific orthopedic implant can be designed based on patient data and imaging data. A healthcare provider can provide feedback for a design of the patient-specific orthopedic implant, treatment protocol, or other aspects of treatment. The patient can provide data and feedback.
1 . A computer-implemented method comprising:
providing access, via at least one user device portal of an implant design system, to an interactive graphical user interface (GUI), wherein the implant design system is configured to
generate, using at least one trained machine learning module, at least a portion of a treatment plan that includes
a pre-operative pathology of a spine of a patient, and
a planned corrected spinal anatomy of the spine of the patient after implantation of a patient-specific spinal implant,
determining, using the at least one trained machine learning module, planned corrected measurements of the spine based on a virtual model of the planned corrected spinal anatomy of the patient, and
design, using the at least one trained machine learning module, the patient-specific spinal implant to fit the planned corrected spinal anatomy of the patient;
displaying, via the interactive GUI of the at least one user device portal,
a pre-operative pathology image representing the pre-operative pathology of the spine of the patient, and
a planned corrected anatomical image and the planned corrected measurements of the spine of the patient, wherein the planned corrected anatomical image shows the virtual model of the planned corrected spinal anatomy of the patient and a representation of the patient-specific spinal implant virtually implanted within the spine of the patient to achieve the planned corrected spinal anatomy, wherein the interactive GUI is configured to receive user input for modifying the planned corrected spinal anatomy and the patient-specific spinal implant; and
after the patient-specific spinal implant is implanted in the patient,
receiving images of the spine of the patient associated with the treatment plan,
measuring, using the at least one trained machine learning module, at least a portion of the patient's anatomy in the images of the spine of the patient associated with the treatment plan to obtain one or more measurements; and
retraining the at least one trained machine learning module based on the one or more measurement.
2 . The computer-implemented method of claim 1 , further comprising causing the interactive GUI to simultaneously display the pre-operative pathology image and the planned corrected anatomical image for visual comparison by a user, wherein the interactive GUI includes:
a first button selectable to display a plurality of pre-operative metrics, and
a second button selectable to display the planned corrected measurements.
3 . The computer-implemented method of claim 1 , wherein the images of the spine include a post-operative image of the patient, the computer-implemented method further comprises determining:
a pre-operative spinal metric for the pre-operative pathology of the patient,
a planned spinal metric of the planned corrected spinal anatomy of the patient represented by the planned corrected anatomical image, and
a post-operative spinal metric based on the post-operative image.
4 . The computer-implemented method of claim 1 , further comprising causing a user device to concurrently display pre-operative metrics associated with the pre-operative pathology, the planned corrected anatomical image, and the planned corrected measurements.
5 . The computer-implemented method of claim 1 , further comprising:
linking the at least one user device portal to a database to enable user navigation for viewing patient data from multiple patients to evaluate the treatment plan.
6 . The computer-implemented method of claim 1 , further comprising displaying, via the interactive GUI,
the pre-operative pathology image and the planned corrected anatomical image for visual comparison by a user associated with the at least one user device portal, and
one or more user inputs selectable to control display of a plurality of pre-operative metrics and the planned corrected measurements, which are associated with the virtual model.
7 . The computer-implemented method of claim 1 , wherein the at least one user device portal includes:
a patient portal configured to receive, via a patient device, first data from the patient to be added to a case data of the patient; and
a healthcare portal configured to receive, via a healthcare provider device, second data from a healthcare provider to be added to the case data.
8 . The computer-implemented method of claim 1 , wherein retraining of the at least one trained machine learning module is based on scoring between a modeled result for the treatment plan and an actual post-operative result of the patient.
9 . The computer-implemented method of claim 1 , wherein the patient-specific spinal implant includes at least one cage, rod, plate, or spinal fusion device.
10 . The computer-implemented method of claim 1 , wherein the planned corrected spinal anatomy of the patient is based a plurality patient-specific implants being implanted in the patient.
11 . A system comprising:
one or more processors; and
one or more memories storing instructions that, when executed by the one or more processors, cause the system to perform a process comprising:
providing access, via at least one user device portal of an implant design system, to an interactive graphical user interface (GUI), wherein the implant design system is configured to
generate, using at least one trained machine learning module,
at least a portion of a treatment plan that includes
a pre-operative pathology of a spine of a patient, and
a planned corrected spinal anatomy of the spine of the patient after implantation of a patient-specific spinal implant,
design, using the at least one trained machine learning module, the patient-specific spinal implant to fit the planned corrected spinal anatomy of the patient;
displaying, via the interactive GUI of the at least one user device portal,
a pre-operative pathology image representing the pre-operative pathology of the spine of the patient, and
a planned corrected anatomical image and associated planned corrected measurements of the spine of the patient, wherein the planned corrected anatomical image shows a virtual model of the planned corrected spinal anatomy of the patient and a representation of the patient-specific spinal implant virtually implanted within the spine of the patient to achieve the planned corrected spinal anatomy, wherein the interactive GUI is configured to receive user input for modifying the planned corrected spinal anatomy and the patient-specific spinal implant; and
after the patient-specific spinal implant is implanted in the patient,
receiving images of the spine of the patient associated with the treatment plan,
measuring, using the at least one trained machine learning module, at least a portion of the patient's anatomy in the images of the spine of the patient associated with the treatment plan to obtain one or more measurements, and
retraining the at least one trained machine learning module based on the one or more measurements.
12 . The system of claim 11 , wherein the process further comprises:
causing the interactive GUI to simultaneously display the pre-operative pathology image and the planned corrected anatomical image for visual comparison by a user, wherein the interactive GUI includes:
a first button selectable to display a plurality of pre-operative metrics, and
a second button selectable to display the planned corrected measurements.
13 . The system of claim 11 , wherein the images of the spine include a post-operative image of the patient, wherein the process further comprises determining:
a pre-operative spinal metric for the pre-operative pathology of the patient,
a planned spinal metric of the planned corrected spinal anatomy of the patient represented by the planned corrected anatomical image, and
a post-operative spinal metric based on the post-operative image.
14 . The system of claim 11 , wherein the process further comprises:
causing a user device to concurrently display pre-operative metrics associated with the pre-operative pathology, the planned corrected anatomical image, and the planned corrected measurements.
15 . The system of claim 11 , wherein the process further comprises:
linking the at least one user device portal to a database to enable user navigation for viewing patient data from multiple patients to evaluate the treatment plan.
16 . The system of claim 11 , wherein the process further comprises:
displaying, via the interactive GUI,
the pre-operative pathology image and the planned corrected anatomical image for visual comparison by a user associated with the at least one user device portal, and
one or more user inputs selectable to control display of a plurality of pre-operative metrics and the planned corrected measurements, which are associated with the virtual model.
17 . The system of claim 11 , wherein the at least one user device portal includes:
a patient portal configured to receive, via a patient device, first data from the patient to be added to a case data of the patient; and
a healthcare portal configured to receive, via a healthcare provider device, second data from a healthcare provider to be added to the case data.
18 . The system of claim 11 , wherein retraining of the at least one trained machine learning module is based on scoring between a modeled result for the treatment plan and an actual post-operative result of the patient.
19 . The system of claim 11 , wherein the patient-specific spinal implant includes at least one cage, rod, plate, or spinal fusion device.
20 . The system of claim 11 , wherein the planned corrected spinal anatomy of the patient is based a plurality patient-specific implants being implanted in the patient.
21 . A non-transitory computer-readable medium storing instructions that, when executed by a computing system, cause the computing system to perform operations comprising:
providing access, via at least one user device portal of an implant design system, to an interactive graphical user interface (GUI), wherein the implant design system is configured to
generate, using at least one trained machine learning module, at least a portion of a treatment plan that includes
a pre-operative pathology of a spine of a patient, and
a planned corrected spinal anatomy of the spine of the patient after implantation of a patient-specific spinal implant,
design, using the at least one trained machine learning module, the patient-specific spinal implant to fit the planned corrected spinal anatomy of the patient;
displaying, via the interactive GUI of the at least one user device portal,
a pre-operative pathology image representing the pre-operative pathology of the spine of the patient, and
a planned corrected anatomical image and associated planned corrected measurements of the spine of the patient, wherein the planned corrected anatomical image shows a virtual model of the planned corrected spinal anatomy of the patient and a representation of the patient-specific spinal implant virtually implanted within the spine of the patient to achieve the planned corrected spinal anatomy, wherein the interactive GUI is configured to receive user input for modifying the planned corrected spinal anatomy and the patient-specific spinal implant; and
after the patient-specific spinal implant is implanted in the patient,
receiving images of the spine of the patient associated with the treatment plan,
measuring, using the at least one trained machine learning module, at least a portion of the patient's anatomy in the images of the spine of the patient associated with the treatment plan to obtain one or more measurements, and
retraining the at least one trained machine learning module based on the one or more measurements.
22 . The non-transitory computer-readable medium of claim 21 , wherein the operations further comprise:
causing the interactive GUI to simultaneously display the pre-operative pathology image and the planned corrected anatomical image for visual comparison by a user, wherein the interactive GUI includes:
a first button selectable to display a plurality of pre-operative metrics, and
a second button selectable to display the planned corrected measurements.
23 . The non-transitory computer-readable medium of claim 21 , wherein the images of the spine include a post-operative image of the patient, wherein the operations further comprise determining:
a pre-operative spinal metric for the pre-operative pathology of the patient,
a planned spinal metric of the planned corrected spinal anatomy of the patient represented by the planned corrected anatomical image, and
a post-operative spinal metric based on the post-operative image.
24 . The non-transitory computer-readable medium of claim 21 , wherein the operations further comprise:
causing a user device to concurrently display pre-operative metrics associated with the pre-operative pathology, the planned corrected anatomical image, and the planned corrected measurements.
25 . The non-transitory computer-readable medium of claim 21 , wherein the operations further comprise:
linking the at least one user device portal to a database to enable user navigation for viewing patient data from multiple patients to evaluate the treatment plan.
26 . The non-transitory computer-readable medium of claim 21 , wherein the operations further comprise:
displaying, via the interactive GUI,
the pre-operative pathology image and the planned corrected anatomical image for visual comparison by a user associated with the at least one user device portal, and
one or more user inputs selectable to control display of a plurality of pre-operative metrics and the planned corrected measurements, which are associated with the virtual model.
27 . The non-transitory computer-readable medium of claim 21 , wherein the at least one user device portal includes:
a patient portal configured to receive, via a patient device, first data from the patient to be added to a case data of the patient; and
a healthcare portal configured to receive, via a healthcare provider device, second data from a healthcare provider to be added to the case data.
28 . The non-transitory computer-readable medium of claim 21 , wherein retraining of the at least one trained machine learning module is based on scoring between a modeled result for the treatment plan and an actual post-operative result of the patient.
29 . The non-transitory computer-readable medium of claim 21 , wherein the patient-specific spinal implant includes at least one cage, rod, plate, or spinal fusion device.
30 . The non-transitory computer-readable medium of claim 21 , wherein the planned corrected spinal anatomy of the patient is based a plurality patient-specific implants being implanted in the patient.
31 . A computer-implemented method comprising:
providing access, via at least one user device portal of a treatment design system, to an interactive graphical user interface (GUI), wherein the treatment design system is configured to
generate, using at least one trained machine learning module, at least a portion of a treatment plan that includes
a pre-operative pathology of a spine of a patient, and
a planned corrected spinal anatomy of the spine of the patient after implantation of a spinal implant,
determining, using the at least one trained machine learning module, planned corrected measurements of the spine based on a virtual model of the planned corrected spinal anatomy of the patient, and
determining, using the at least one trained machine learning module, the spinal implant configured to achieve the planned corrected spinal anatomy of the patient;
after the spinal implant is implanted in the patient,
receiving images of the spine of the patient associated with the treatment plan,
measuring, using the at least one trained machine learning module, at least a portion of the patient's anatomy in the images to obtain one or more post-operative measurements; and
retraining the at least one trained machine learning module based on the one or more post-operative measurements.