US-12622786-B2 - Patient-specific implant design, manufacture and platform with clinical and third-party data acquisition

US12622786B2US 12622786 B2US12622786 B2US 12622786B2US-12622786-B2

Abstract

Systems and methods for designing patient-specific orthopedic implants. The systems and methods can include automated collection protocols for available third-party data to use to generate surgical plans or patient-specific orthopedic implant designs. The system can identify triggers for collecting third-party data. The system can determine a confidence score for the collected third-party data based on the type of data, the source of the data, and the similarity of the data to patient data. The system can select parameters in the third-party data to integrate into designing a patient-specific implant or patient treatment.

Inventors

Niall Patrick Casey
Sarah JAUREGUI
Jeremy Winston

Assignees

CARLSMED, INC.

Dates

Publication Date: 20260512
Application Date: 20250606

Claims (20)

1 . A method of manufacturing and implanting a patient-specific interbody cage, the method comprising: generating a first cage design of a first patient-specific interbody cage configured to be positioned between an upper vertebra and a lower vertebra to achieve a first anatomical correction for a spine of a subject when implanted in the subject, wherein the first patient-specific interbody cage has an upper contoured porous surface to contact a lower vertebral endplate of the upper vertebra, and a lower contoured porous surface to contact an upper endplate of the lower vertebra; performing at least one design check on the first cage design; determining patient data of the subject includes at least one edge case pathology based on the at least one design check; in response to determining the patient data includes the at least one edge case pathology, collecting, via a network connection, third-party data from one or more third-party sources on remote servers, wherein the third-party data is associated with the at least one edge case pathology; determining a confidence score for the third-party data based on one or more characteristics of the one or more third-party sources or the third-party data; in response to the confidence score being above a threshold, selecting one or more parameters from the third-party data; and generating a second cage design for the first patient-specific interbody cage based on the patient data and the one or more parameters from the third-party data; and after determining the second cage design meets a design threshold, manufacturing the first patient-specific interbody cage according to the second cage design; and accessing a disc space between the upper vertebra and the lower vertebra; implanting the first patient-specific interbody cage at the disc space.
2 . The method of claim 1 , further comprising providing one or more bone screws for anchoring the first patient-specific interbody cage to at least one of the upper vertebra and or the lower vertebra.
3 . The method of claim 1 , further comprising designing and manufacturing a spinal rod for producing the first anatomical correction.
4 . The method of claim 1 , further comprising: performing a comparison of the third-party data to the patient data; identifying a subset of the third-party data that is associated with the patient data; and storing the subset of the third-party data in at least one database for use to generate the second cage design.
5 . The method of claim 1 , further comprising: determining at least one dimension and at least one surface feature of the second cage design based on the one or more parameters of the third-party data; and generating manufacturing instructions for the second cage design with the at least one dimension and the at least one surface feature.
6 . A method comprising: generating a first patient-specific orthopedic implant design based on patient data associated with a patient; performing at least one design check on the first patient-specific orthopedic implant design; determining the patient data includes at least one edge case pathology based on the at least one design check; in response to determining the patient data includes the at least one edge case pathology, collecting, via a network connection, third-party data from one or more third-party sources on remote servers, wherein the third-party data is associated with the at least one edge case pathology; determining a confidence score for the third-party data based on one or more characteristics of one or more sources or the third-party data; in response to the confidence score being above a threshold, selecting one or more parameters from the third-party data; and generating a second patient-specific orthopedic implant design based on the patient data and the one or more parameters from the third-party data.
7 . The method of claim 6 , further comprising: performing a comparison of the third-party data to the patient data; identifying a subset of the third-party data that is associated with the patient data; and storing the subset of the third-party data in at least one database.
8 . The method of claim 6 , further comprising: determining at least one dimension and at least one surface feature of the second patient-specific orthopedic implant design based on the one or more parameters of the third-party data; and generating manufacturing instructions for the second patient-specific orthopedic implant design with the at least one dimension and the at least one surface feature.
9 . The method of claim 6 , further comprising: training at least one machine learning model to identify at least one trigger for collecting third-party data from the one or more sources; inputting, into the at least one machine learning model, one or more design constraints for a patient-specific implant; and identifying, by the at least one machine learning model, a subset of the third-party data that complies with the one or more design constraints.
10 . The method of claim 6 , further comprising: integrating the third-party data with the patient data in at least one database; tagging a first subset of the third-party data associated with the one or more parameters; and removing, from the at least one database, a second subset of the third-party data, wherein the first subset is different from the second subset.
11 . The method of claim 6 , further comprising: identifying at least one trigger to collect the third-party data from the one or more sources; determining a subset of the third-party data associated with the patient data based on at least one of: an anatomical feature of the patient, a demographic of the patient, and an activity level of the patient; and sending a notification to a user regarding the subset of the third-party data, wherein the notification includes one or more links to the subset of the third-party data.
12 . The method of claim 6 , further comprising: determining at least one similarity between the patient data and the third-party data; and displaying, via a user interface, the at least one similarity.
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: generating a first patient-specific orthopedic implant design based on patient data associated with a patient; performing at least one design check on the first patient-specific orthopedic implant design; determining the patient data includes at least one edge case pathology based on the at least one design check; in response to determining the patient data includes the at least one edge case pathology, collecting, via a network connection, third-party data from one or more third-party sources on remote servers, wherein the third-party data is associated with the at least one edge case pathology; determining a confidence score for the third-party data based on one or more characteristics of one or more sources or the third-party data; in response to the confidence score being above a threshold, selecting one or more parameters from the third-party data; and generating a second patient-specific orthopedic implant design based on the patient data and the one or more parameters from the third-party data.
14 . The system according to claim 13 , wherein the process further comprises: performing a comparison of the third-party data to the patient data; identifying a subset of the third-party data that is associated with the patient data; and storing the subset of the third-party data in at least one database.
15 . The system according to claim 13 , wherein the process further comprises: determining at least one dimension and at least one surface feature of the second patient-specific orthopedic implant design based on the one or more parameters of the third-party data; and generating manufacturing instructions for the second patient-specific orthopedic implant design with the at least one dimension and the at least one surface feature.
16 . The system according to claim 13 , wherein the process further comprises: training at least one machine learning model to identify at least one trigger for collecting third-party data from the one or more sources; inputting, into the at least one machine learning model, one or more design constraints for a patient-specific implant; and identifying, by the at least one machine learning model, a subset of the third-party data that complies with the one or more design constraints.
17 . The system according to claim 13 , wherein the process further comprises: integrating the third-party data with the patient data in at least one database; tagging a first subset of the third-party data associated with the one or more parameters; and removing, from the at least one database, a second subset of the third-party data, wherein the first subset is different from the second subset.
18 . The system according to claim 13 , wherein the process further comprises: identifying at least one trigger to collect the third-party data from the one or more sources; determining a subset of the third-party data associated with the patient data based on at least one of: an anatomical feature of the patient, a demographic of the patient, and an activity level of the patient; and sending a notification to a user regarding the subset of the third-party data, wherein the notification includes one or more links to the subset of the third-party data.
19 . The system according to claim 13 , wherein the process further comprises: determining at least one similarity between the patient data and the third-party data; and displaying, via a user interface, the at least one similarity.
20 . A non-transitory computer-readable medium storing instructions that, when executed by a computing system, cause the computing system to perform operations comprising: generating a first patient-specific orthopedic implant design based on patient data associated with a patient; performing at least one design check on the first patient-specific orthopedic implant design; determining the patient data includes at least one edge case pathology based on the at least one design check; in response to determining the patient data includes the at least one edge case pathology, collecting, via a network connection, third-party data from one or more third-party sources on remote servers, wherein the third-party data is associated with the at least one edge case pathology; determining a confidence score for the third-party data based on one or more characteristics of one or more sources or the third-party data; in response to the confidence score being above a threshold, selecting one or more parameters from the third-party data; and generating a second patient-specific orthopedic implant design based on the patient data and the one or more parameters from the third-party data.

Description

CROSS-REFERENCE TO RELATED APPLICATION This application is a non-provisional of and claims priority to U.S. Provisional Patent Application No. 63/657,725, filed Jun. 7, 2024 entitled “PATIENT-SPECIFIC IMPLANT DESIGN AND PLATFORM WITH CLINICAL DATA ACQUISITION,” which is hereby incorporated by reference in its entirety for all purposes. TECHNICAL FIELD The present disclosure is generally related to designing, manufacturing, and implementing medical care, and more particularly to systems and methods for designing and manufacturing patient-specific implants. BACKGROUND Numerous types of data associated with patient treatments and surgical interventions are available. To determine treatment protocols for a patient, physicians often rely on a subset of patient data available via the patient's medical record and historical outcome data. However, the amount of patient data and historical data may be limited, and the available data may not be correlated or relevant to the particular patient to be treated. Conventional technologies in the field of orthopedics may lack the capability to draw upon large data sets to generate and optimize patient-specific treatments (e.g., surgical interventions and/or implant designs) to achieve favorable treatment outcomes. Unfortunately, during a patient-specific treatment there is no current way to actively collect and analyze third-party data related to the patient. BRIEF DESCRIPTION OF THE DRAWINGS The accompanying drawings illustrate various embodiments of systems, methods, and embodiments of various other aspects of the disclosure. Any person with ordinary skill in the art will appreciate that the illustrated element boundaries (e.g., boxes, groups of boxes, or other shapes) in the figures represent one example of the boundaries. It may be that in some examples one element may be designed as multiple elements or that multiple elements may be designed as one element. In some examples, an element shown as an internal component of one element may be implemented as an external component in another, and vice versa. Furthermore, elements may not be drawn to scale. Non-limiting and non-exhaustive descriptions are described with reference to the following drawings. The components in the figures are not necessarily to scale, emphasis instead being placed upon illustrating principles. FIG. 1 is a network connection diagram illustrating a system for providing patient-specific medical care, according to an embodiment. FIG. 2 illustrates a computing device suitable for use in connection with the system of FIG. 1, according to an embodiment. FIG. 3 is a flow diagram illustrating a method for providing patient-specific medical care, according to an embodiment. FIGS. 4A-4C illustrate exemplary data sets that may be used and/or generated in connection with the methods described herein, according to an embodiment. FIG. 4A illustrates a patient data set. FIG. 4B illustrates a plurality of reference patient data sets. FIG. 4C illustrates similarity scores and outcome scores for the reference patient data sets of FIG. 4B. FIG. 5 is a flow diagram illustrating another method for providing patient-specific medical care, according to an embodiment. FIG. 6A is a flow diagram illustrating a method for collecting third-party data to provide patient-specific medical care, according to an embodiment. FIG. 6B is a flow diagram illustrating a method for determining outcome-driven parameters for implant design and treatment. FIG. 6C is a flow diagram illustrating a method for managing patient data to provide patient-specific medical care, according to an embodiment. FIGS. 7A-7D illustrate an exemplary patient data set that may be used and/or generated in connection with the methods described herein, according to an embodiment. FIGS. 8A and 8B illustrate an exemplary virtual model of a patient's spine that may be used and/or generated in connection with the methods described herein, according to an embodiment. FIGS. 9A-1-9B-2 illustrate an exemplary virtual model of a patient's spine in a pre-operative anatomical configuration and a corrected anatomical configuration. More specifically, FIGS. 9A-1 and 9A-2 illustrate the pre-operative anatomical configuration of the patient, and FIGS. 9B-1 and 9B-2 illustrate the corrected anatomical configuration. FIG. 10A illustrates an exemplary interactive surgical plan for a patient-specific surgical procedure, according to an embodiment. FIG. 10B illustrates pre-operative, plan, intra-operative, and post-operative images to allow for assessment of achievement of surgical goals, according to an embodiment. FIG. 10C illustrates a graphical user interface (GUI) for visually depicting various aspects of third-party data, according to an embodiment. FIG. 11 illustrates an exemplary surgical plan report that details the surgical plan for surgeon review and that may be used and/or generated in connection with the methods described herein, according to an embodiment. FIGS. 12A a