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) having a viewable image of a virtual model of a spine of a patient, one or more user inputs for selecting display of at least one of a plurality of pre-operative metrics or a plurality of post-operative metrics associated with the virtual model,
wherein the at least one user device portal is linked to case data such that the interactive GUI provides access to at least a portion of the case data;
wherein the implant design system is configured to be used to generate, at least partially using at least one trained machine learning model, a treatment plan that includes
a pre-operative pathology of the patient, and
a planned corrected anatomy of the patient after installation of a patient-specific implant;
receiving, via a user-selectable element of the interactive GUI, approval of the treatment plan;
after the patient-specific implant designed by the implant design system is implanted in the patient, receiving, via the at least one user device portal, one or more post-operative images of the patient;
displaying, via the at least one user device portal, a post-operative review interface including
a pre-operative pathology image and associated pre-operative metrics of the patient, and
a planned corrected anatomical image and associated planned corrected metrics of the patient, wherein the planned corrected anatomical image shows the virtual model of the spine, in a corrected position, with a representation of the patient-specific implant; and
retraining the at least one trained machine learning model using at least a portion of the received one or more post-operative images indicative of an outcome of the treatment plan, wherein the at least one retrained machine learning model is configured to receive new patient data for another patient and to generate a new treatment plan for the another patient based on the new patient data.
2. The computer-implemented method of claim 1 , further comprising:
linking the at least one user device portal to a patient identifier, a surgery, and the patient-specific implant for the surgery; and
linking the at least one user device portal to multiple patients for viewing multiple patient data.
3. The computer-implemented method of claim 1 , wherein the interactive GUI includes a window that concurrently displays an image of a pre-operative pathology of the patient and an image of a post-operative anatomy of the patient for visual comparison by a user associated with the at least one user device portal, wherein the one or more user inputs includes a first button for selecting display of the plurality of pre-operative metrics and a second button for selecting display of the plurality of post-operative metrics associated with the virtual model.
4. The computer-implemented method of claim 1 , wherein the at least one user device portal includes:
a patient portal for receiving first data from the patient to be added to the case data; and
a healthcare portal for receiving second data from a healthcare provider to be added to the case data.
5. The computer-implemented method of claim 1 , wherein retraining of the at least one trained machine learning model is based on a similarity score between a modeled result for the treatment plan and an actual post-operative result of the patient.
6. The computer-implemented method of claim 1 , wherein the patient-specific implant is a cage, a rod, a plate, or a spinal fusion device.
7. The computer-implemented method of claim 1 , wherein the planned corrected anatomical image includes an image of a virtual model representing an anatomy of the patient in a corrected configuration and annotation for the patient-specific implant.
8. The computer-implemented method of claim 1 , wherein the viewable image of the virtual model includes at least one of a first image representing pre-operative pathology of the patient or a second image representing a corrected anatomy of the patient.
9. The computer-implemented method of claim 1 , further comprising:
receiving post-operative data including at least one of a set of post-operative images of the patient, physician feedback, patient recovery level, recovery time, or result after a period of time; and
comparing at least a portion of the post-operative data to the treatment plan, wherein selection of the portion of the received post-operative data as one or more training items is based on the comparison, wherein the one or more training items are inputted into the at least one trained machine learning model for the retraining.
10. The computer-implemented method of claim 1 , further comprising:
receiving post-operative data of the patient; and
determining spinal metrics for comparing a pre-operative pathology of the patient, a planned correction of the patient, and a post-operative anatomy of the patient, wherein the post-operative anatomy is based on the post-operative data.
11. The computer-implemented method of claim 1 ,
wherein displaying the pre-operative metrics and the planned corrected metrics includes concurrently displaying the pre-operative metrics in a first column and the planned corrected metrics in a second column.
12. The computer-implemented method of claim 11 , wherein corresponding metrics of the pre-operative metrics and the planned corrected metrics are aligned horizontally.
13. 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) having a viewable image of a virtual model of a spine of a patient, and one or more user inputs for selecting display of at least one of a plurality of pre-operative metrics or a plurality of post-operative metrics associated with the virtual model,
wherein the at least one user device portal is linked to case data such that the interactive GUI provides access to at least a portion of the case data;
wherein the implant design system is configured to be used to generate, at least partially using at least one trained machine learning model, a treatment plan that includes
a pre-operative pathology of the patient, and
a planned corrected anatomy of the patient after installation of a patient-specific implant;
receiving, via a user-selectable element of the interactive GUI, approval of the treatment plan;
after the patient-specific implant designed by the implant design system is implanted in the patient, receiving, via the at least one user device portal, one or more post-operative images of the patient; and
displaying, via the at least one user device portal, a post-operative review interface including
a pre-operative pathology image and associated pre-operative metrics of the patient, and
a planned corrected anatomical image and associated planned corrected metrics of the patient, wherein the planned corrected anatomical image shows the virtual model of the spine, in a corrected position, with a representation of the patient-specific implant,
retraining the at least one trained machine learning model using at least a portion of the received one or more post-operative images indicative of an outcome of the treatment plan, wherein the at least one retrained machine learning model is configured to receive new patient data for another patient and to generate a new treatment plan for the another patient based on the new patient data.
14. The system of claim 13 , wherein the process further comprises:
linking the at least one user device portal to a patient identifier, a surgery, and the patient-specific implant for the surgery; and
linking the at least one user device portal to multiple patients for viewing multiple patient data.
15. The system of claim 13 , wherein the interactive GUI includes a window that concurrently displays an image of a pre-operative pathology of the patient and an image of a post-operative anatomy of the patient for visual comparison by a user associated with the at least one user device portal.
16. The system of claim 13 , wherein the at least one user device portal includes:
a patient portal for receiving first data from the patient to be added to the case data; and
a healthcare portal for receiving second data from a healthcare provider to be added to the case data.
17. The system of claim 13 , wherein retraining of the at least one trained machine learning model is based on a similarity score between a modeled result for the treatment plan and an actual post-operative result of the patient.
18. The system of claim 13 , wherein the patient-specific implant is a cage, a rod, a plate, or a spinal fusion device.
19. The system of claim 13 , wherein the planned corrected anatomical image includes an image of a virtual model representing an anatomy of the patient in a corrected configuration and annotation for the patient-specific implant.
20. The system of claim 13 , wherein the viewable image of the virtual model includes at least one of a first image representing pre-operative pathology of the patient or a second image representing a corrected anatomy of the patient.
21. The system of claim 13 , wherein the process further comprises:
receiving post-operative data including at least one of a set of post-operative images of the patient, physician feedback, patient recovery level, recovery time, or result after a period of time; and
comparing at least a portion of the post-operative data to the treatment plan, wherein selection of the portion of the received post-operative data as one or more training items is based on the comparison, wherein the one or more training items are inputted into the at least one trained machine learning model for the retraining.
22. The system of claim 13 , wherein the process further comprises:
receiving post-operative data of the patient; and
determining spinal metrics for comparing a pre-operative pathology of the patient, a planned anatomy of the patient, and a post-operative anatomy of the patient, wherein the post-operative anatomy is based on the post-operative data.
23. 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) having a viewable image of a virtual model of a spine of a patient, and one or more user inputs for selecting display of at least one of a plurality of pre-operative metrics or a plurality of post-operative metrics associated with the virtual model,
wherein the at least one user device portal is linked to case data such that the interactive GUI provides access to at least a portion of the case data;
wherein the implant design system is configured to be used to generate, at least partially using at least one trained machine learning model, a treatment plan that includes
a pre-operative pathology of the patient, and
a planned corrected anatomy of the patient after installation of a patient-specific implant;
receiving, via a user-selectable element the interactive GUI, approval of the treatment plan;
after the patient-specific implant designed by the implant design system is implanted in the patient, receiving, via the at least one user device portal, one or more post-operative images of the patient;
displaying, via the at least one user device portal, a post-operative review interface including
a pre-operative pathology image and associated pre-operative metrics of the patient, and
a planned corrected anatomy image and associated planned corrected metrics of the patient, wherein the planned corrected anatomy image shows the virtual model of the spine, in a corrected position, with a representation of the patient-specific implant; and
retraining the at least one trained machine learning model using at least a portion of the received one or more post-operative images indicative of an outcome of the treatment plan, wherein the at least one retrained machine learning model is configured to receive new patient data for another patient and to generate a new treatment plan for the another patient based on the new patient data.
24. The non-transitory computer-readable medium of claim 23 , wherein the operations further comprise:
linking the at least one user device portal to a patient identifier, a surgery, and the patient-specific implant for the surgery; and
linking the at least one user device portal to multiple patients for viewing multiple patient data.
25. The non-transitory computer-readable medium of claim 23 , wherein the interactive GUI includes a window that concurrently displays an image of a pre-operative pathology of the patient and an image of a post-operative anatomy of the patient for visual comparison by a user associated with the at least one user device portal.
26. The non-transitory computer-readable medium of claim 23 , wherein the at least one user device portal includes:
a patient portal for receiving first data from the patient to be added to the case data; and
a healthcare portal for receiving second data from a healthcare provider to be added to the case data.
27. The non-transitory computer-readable medium of claim 23 , wherein retraining of the at least one trained machine learning model is based on a similarity score between a modeled result for the treatment plan and an actual post-operative result of the patient.
28. The non-transitory computer-readable medium of claim 23 , wherein the patient-specific implant is a cage, a rod, a plate, or a spinal fusion device.
29. The non-transitory computer-readable medium of claim 23 , wherein the planned corrected anatomy image includes an image of a virtual model representing an anatomy of the patient in a corrected configuration and annotation for the patient-specific implant.
30. The non-transitory computer-readable medium of claim 23 , wherein the viewable image of the virtual model includes at least one of a first image representing pre-operative pathology of the patient or a second image representing a corrected anatomy of the patient.
31. The non-transitory computer-readable medium of claim 23 , wherein the operations further comprise:
receiving post-operative data including at least one of a set of post-operative images of the patient, physician feedback, patient recovery level, recovery time, or result after a period of time; and
comparing at least a portion of the post-operative data to the treatment plan, wherein selection of the portion of the received post-operative data as one or more training items is based on the comparison, wherein the one or more training items are inputted into the at least one machine learning model for the retraining.
32. The non-transitory computer-readable medium of claim 23 , wherein the operations further comprise:
receiving post-operative data of the patient; and
determining spinal metrics for comparing a pre-operative pathology of the patient, a planned anatomy of the patient, and a post-operative anatomy of the patient, wherein the post-operative anatomy is based on the post-operative data.
33. 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 be used to generate, using at least one trained machine learning model, at least a portion of a treatment plan, wherein the treatment plan includes
a pre-operative pathology of a patient, and
a planned corrected anatomy of the patient after installation of a patient-specific implant;
displaying, via the at least one user device portal, a review interface of the GUI including
a pre-operative pathology image, and
a planned corrected anatomical image and associated planned corrected metrics of the patient, wherein the planned corrected anatomical image shows a virtual model of a spine, in a corrected configuration, with a representation of the patient-specific implant; and
after the patient-specific implant is implanted in the patient,
receiving data associated with a patient outcome of the treatment plan; and
retraining the at least one trained machine learning model based on at least a portion of the data associated with the patient outcome of the treatment plan.
34. The computer-implemented method of claim 33 , wherein
the interactive GUI includes a window that concurrently displays an image of a pre-operative pathology of the patient and the planned corrected anatomical image for visual comparison by a user, wherein the interactive GUI includes:
a first button for selecting display of a plurality of pre-operative metrics, and
a second button for selecting display of a plurality of planned post-operative metrics associated with the virtual model.
35. The computer-implemented method of claim 33 , wherein the data associated with the patient outcome includes post-operative image data of the patient, the 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 anatomy of the patient represented by the planned corrected anatomical image, and
a post-operative spinal metric based on the post-operative image data.
36. The computer-implemented method of claim 33 , further comprising concurrently displaying pre-operative metrics associated with the pre-operative pathology, the planned corrected anatomical image, and the planned corrected metrics.