COMPUTERIZED PREDICTION OF HUMERAL PROSTHESIS FOR SHOULDER SURGERY

1 . A method comprising:

obtaining, by a surgical assistance system, patient-specific values of a plurality of physical characteristics of a humeral bone of a patient; and

predicting, by the surgical assistance system, based on the patient-specific values, a humeral prosthesis for the patient from among a plurality of humeral prostheses, wherein the predicted humeral prosthesis has a filling ratio less than a remodeling threshold for the patient, wherein:

the filling ratio of the humeral prosthesis is a ratio of: (i) a radial distance from a lengthwise central axis of a stem of the humeral prosthesis to an outer surface of the stem, to (ii) a radial distance from the lengthwise central axis of the stem to an inner surface of an intramedullary canal of a humerus of the patient, and

the remodeling threshold for the patient is a filling ratio above which the predicted humeral prosthesis would cause bone remodeling in the patient.

2 . The method of claim 1 , wherein:

the method comprises storing a plurality of machine-learned coefficients, and

predicting the humeral prosthesis comprises determining a humeral prosthesis index of the predicted humeral implant as a sum of a plurality of elements and a machine-learned constant, each respective element of the plurality of elements being a multiplication product of a respective machine-learned coefficient in the plurality of machine-learned coefficients and a corresponding physical characteristic in the plurality of physical characteristics of the humeral bone of the patient.

3 . The method of claim 2 , wherein the machine-learned coefficients are machine learned using a regression from humeral prosthesis indexes of humeral prostheses implanted in patients with filling ratios of the humeral prostheses that are less than remodeling thresholds for the patients.

4 . The method of claim 1 , wherein:

a region at a diaphysis of the humeral bone is partitioned into a first set of blocks, a region at a metaphysis of the humeral bone is partitioned into a second set of blocks, obtaining the patient-specific values for the plurality of physical characteristics comprises:

calculating a first value as an average of Hounsfield units of the first set of blocks that exceed a first threshold,

calculating a second value as an average of Hounsfield units of the second set of blocks that exceed a second threshold, and

calculating a third value as an average of Hounsfield units of the first set of blocks that exceed a third threshold different from the first threshold.

5 . The method of claim 4 , wherein the plurality of physical characteristics consists of the first value, the second value, and the third value.

6 . The method of claim 1 , wherein predicting the humeral prosthesis comprises predicting, by the surgical assistance system, the humeral prosthesis based on the patient-specific values of the plurality of physical characteristics of the humeral bone and based on an age of the patient.

7 . The method of claim 1 , wherein the patient-specific values regarding the physical characteristics of the humeral bone of the patient include one or more shape parameters based on changes to shape parameters of a mean statistical shape model (SSM) of a generic humeral bone to conform the mean SSM to the humeral bone of the patient

8 . A method comprising:

obtaining, by a surgical assistance system, patient-specific values of a plurality of patient-specific values, wherein:

the plurality of patient-specific values includes patient-specific values of a plurality of physical characteristics of a humeral bone of a patient, and

the plurality of physical characteristics includes (i) an average cortical metaphyseal bone density in Hounsfield units that exceed a first threshold, (ii) an average cortical spongious bone density in Hounsfield units that are less than or equal to a second threshold, and (iii) one or more shape parameters of a statistical shape model (SSM) of the humeral bone of the patient; and

predicting, by the surgical assistance system, based on the patient-specific values, a humeral prosthesis for the patient from among a plurality of humeral prostheses.

9 . The method of claim 8 , wherein the patient-specific values further include one or more of an age of the patient, a gender of the patient, or a diagnosis of the patient.

10 . The method of claim 8 , wherein:

the method comprises storing a plurality of machine-learned coefficients, and

predicting the humeral prosthesis comprises determining a humeral prosthesis index of the predicted humeral implant as a sum of a plurality of elements and a machine-learned constant, each respective element of the plurality of elements being a multiplication product of a respective machine-learned coefficient in the plurality of machine-learned coefficients and a corresponding physical characteristic in the plurality of physical characteristics of the humeral bone of the patient.

11 . A computing system comprising:

a memory configured to store medical imaging data; and

processing circuitry configured to:

obtain patient-specific values of a plurality of physical characteristics of a humeral bone of a patient and

predict, based on the patient-specific values, a humeral prosthesis for the patient from among a plurality of humeral prostheses, wherein the predicted humeral prosthesis has a filling ratio less than a remodeling threshold for the patient, wherein:

the filling ratio of the humeral prosthesis is a ratio of: (i) a radial distance from a lengthwise central axis of a stem of the humeral prosthesis to an outer surface of the stem, to (ii) a radial distance from the lengthwise central axis of the stem to an inner surface of an intramedullary canal of a humerus of the patient, and

the remodeling threshold for the patient is a filling ratio above which the predicted humeral prosthesis would cause bone remodeling in the patient.

12 . (canceled)

13 . A non-transitory computer-readable data storage medium having instructions stored thereon that, when executed, cause a computing system to:

obtain patient-specific values of a plurality of physical characteristics of a humeral bone of a patient and

predict, based on the patient-specific values, a humeral prosthesis for the patient from among a plurality of humeral prostheses, wherein the predicted humeral prosthesis has a filling ratio less than a remodeling threshold for the patient, wherein:

the filling ratio of the humeral prosthesis is a ratio of: (i) a radial distance from a lengthwise central axis of a stem of the humeral prosthesis to an outer surface of the stem, to (ii) a radial distance from the lengthwise central axis of the stem to an inner surface of an intramedullary canal of a humerus of the patient, and

the remodeling threshold for the patient is a filling ratio above which the predicted humeral prosthesis would cause bone remodeling in the patient.

14 . The computing system of claim 11 ,

wherein the memory stores a plurality of machine-learned coefficients, and

the processing circuitry is configured to, as part of predicting the humeral prosthesis, determine a humeral prosthesis index of the predicted humeral implant as a sum of a plurality of elements and a machine-learned constant, each respective element of the plurality of elements being a multiplication product of a respective machine-learned coefficient in the plurality of machine-learned coefficients and a corresponding physical characteristic in the plurality of physical characteristics of the humeral bone of the patient.

15 . The computing system of claim 14 , wherein the machine-learned coefficients are machine learned using a regression from humeral prosthesis indexes of humeral prostheses implanted in patients with filling ratios of the humeral prostheses that are less than remodeling thresholds for the patients.

16 . The computing system of claim 11 , wherein:

a region at a diaphysis of the humeral bone is partitioned into a first set of blocks,

a region at a metaphysis of the humeral bone is partitioned into a second set of blocks,

the processing circuitry is configured to, as part of obtaining the patient-specific values for the plurality of physical characteristics:

calculate a first value as an average of Hounsfield units of the first set of blocks that exceed a first threshold,

calculate a second value as an average of Hounsfield units of the second set of blocks that exceed a second threshold, and

calculate a third value as an average of Hounsfield units of the first set of blocks that exceed a third threshold different from the first threshold.

17 . The computing system of claim 16 , wherein the plurality of physical characteristics consists of the first value, the second value, and the third value.

18 . The computing system of claim 11 , wherein predicting the humeral prosthesis comprises predicting, by the surgical assistance system, the humeral prosthesis based on the patient-specific values of the plurality of physical characteristics of the humeral bone and based on an age of the patient.

19 . The computing system of claim 11 , wherein the patient-specific values regarding the physical characteristics of the humeral bone of the patient include one or more shape parameters based on changes to shape parameters of a mean statistical shape model (SSM) of a generic humeral bone to conform the mean SSM to the humeral bone of the patient.

20 . The non-transitory computer-readable data storage medium of claim 13 , wherein the instructions that cause the computing system to predict the humeral prosthesis comprises instructions that, when executed, cause the computing system to determine a humeral prosthesis index of the predicted humeral implant as a sum of a plurality of elements and a machine-learned constant, each respective element of the plurality of elements being a multiplication product of a respective machine-learned coefficient in a plurality of machine-learned coefficients and a corresponding physical characteristic in the plurality of physical characteristics of the humeral bone of the patient.

21 . The non-transitory computer-readable data storage medium of claim 20 , wherein the machine-learned coefficients are machine learned using a regression from humeral prosthesis indexes of humeral prostheses implanted in patients with filling ratios of the humeral prostheses that are less than remodeling thresholds for the patients.