NEURAL NETWORK FOR DIAGNOSIS OF SHOULDER CONDITION

1 . A computer-implemented method comprising:

generating, by a computing system, a plurality of training datasets from past shoulder surgery cases, wherein:

a neural network (NN) has an input layer, an output layer, and one or more hidden layers between the input layer and the output layer,

the input layer includes a plurality of input layer neurons, each input layer neuron in the plurality of input layer neurons corresponding to a different input element in a plurality of input elements,

the output layer includes a plurality of output layer neurons, each output layer neuron in the plurality of output layer neurons corresponding to a different output element in a plurality of output elements, each output element in the plurality of output elements corresponding to a different class in one or more shoulder pathology classification systems;

each respective training dataset corresponds to a different training data patient in a plurality of training data patients and comprises a respective training input vector and a respective target output vector,

for each respective training dataset, the training input vector of the respective training dataset comprises a value for each element of the plurality of input elements,

for each respective training dataset, the target output vector of the respective training dataset comprises a value for each element of the plurality of output elements;

using, by the computing system, the plurality of training datasets to train the NN;

obtaining, by the computing system, a current input vector that corresponds to a current patient;

applying, by the computing system, the NN to the current input vector to generate a current output vector; and

determining, by the computing system, based on the current output vector, a classification of a shoulder condition of the current patient.

2 . The computer-implemented method of claim 1 , wherein:

a confidence value function outputs confidence values, wherein all possible confidence values output by the confidence value function are within a predefined range,

applying the NN to the current input vector to generate the current output vector comprises, for each respective output layer neuron in the plurality of output layer neurons:

calculating, by the computing system, an output value of the respective output layer neuron; and

applying, by the computing system, the confidence value function with the output value of the respective output layer neuron as input to the confidence value function, wherein the confidence value function outputs a confidence value for the respective output layer neuron, and

for each respective output layer neuron in the plurality of output layer neurons:

the output element corresponding to the respective output layer neuron specifies the confidence value for the respective output layer neuron, and

the confidence value for the respective output layer neuron is a measure of confidence that the shoulder condition of the current patient belongs to a class in the one or more shoulder pathology classification systems that corresponds to the output element corresponding to the respective output layer neuron.

3 . The computer-implemented method of claim 2 , wherein the confidence value function is one of: a hyperbolic tangent function or a sigmoid function.

4 . The computer-implemented method of claim 1 , wherein the one or more shoulder pathology classification systems include a primary glenohumeral osteoarthritis classification system.

5 . The computer-implemented method of claim 4 , wherein the primary glenohumeral osteoarthritis classification system is one of: a Walch classification system or a Favard classification system.

6 . The computer-implemented method of claim 1 , wherein the one or more shoulder pathology classification systems include a rotator cuff classification system.

7 . The computer-implemented method of claim 6 , wherein the rotator cuff classification system is one of: a Warner classification system or a Goutalier classification system.

8 . The computer-implemented method of claim 1 , where the one or more shoulder pathology classification systems includes a classification system for one or more of: primary glenoid humeral osteoarthritis (PGHOA), rotator cuff tear arthropathy (RCTA) instability, massive rotator cuff tear (MRCT), rheumatoid arthritis, post-traumatic arthritis, and osteoarthritis.

9 . The computer-implemented method of claim 1 , wherein:

the method further comprises:

obtaining, by the computing system, images of the current patient;

segmenting, by the computing system, the images to identify internal structures of the current patient; and

determining, by the computing system, based on relative positions of the identified internal structures of the current patient, a plurality of measurements, and

the plurality of input elements includes an input element for each measurement in the plurality of measurements.

10 . The computer-implemented method of claim 1 , wherein the plurality of input elements includes measurements of morphological characteristics of the patient.

11 . The computer-implemented method of claim 1 , wherein the plurality of input elements includes inputs elements specifying one or more of:

a glenoid version, a glenoid inclination, a glenoid orientation, glenoid direction, a glenoid best fit sphere radius, a glenoid best fit sphere root mean squares error, a reverse shoulder angle, a critical shoulder angle, acromion humeral space, a glenoid humeral space, glenoid bone density scores for a humerus and a glenoid, humeral version, a humeral neck shaft angle, a humeral head best fit sphere radius and a root mean squares error of the humeral head best fit sphere radius, humeral subluxation, humeral orientation, humeral direction, a fatty infiltration measure of a rotator cuff, an atrophy measure of the rotator cuff, a patient age, patient gender, patient activities, or a patient body mass index.

12 . The computer-implemented method of claim 1 , wherein generating the plurality of training datasets comprises selecting, by the computing system, the plurality of training datasets from a database of training datasets based on one or more training dataset selection criteria.

13 . The computer-implemented method of claim 12 , wherein the one or more training data set selection criteria include one or more of:

which surgeon operated on the plurality of training data patients,

a region in which the training data patients live, or

a region associated with one or more surgeons.

14 . . The method of claim 1 , wherein the one or more training dataset selection criteria include postoperative health outcomes of the training data patients.

15 . The method of claim 14 , wherein the postoperative health outcomes of the training data patients include one or more of: postoperative range of motion, presence of postoperative infection, or postoperative pain.

16 . A computing system comprising:

a storage system configured to store parameters of a neural network (NN) that has an output layer, and one or more hidden layers between the input layer and the output layer; and

one or more processors configured to:

generate a plurality of training datasets from past shoulder surgery cases, wherein:

a neural network (NN) has an input layer, an output layer, and one or more hidden layers between the input layer and the output layer,

the input layer includes a plurality of input layer neurons, each input layer neuron in the plurality of input layer neurons corresponding to a different input element in a plurality of input elements,

the output layer includes a plurality of output layer neurons, each output layer neuron in the plurality of output layer neurons corresponding to a different output element in a plurality of output elements, each output element in the plurality of output elements corresponding to a different class in one or more shoulder pathology classification systems;

each respective training dataset corresponds to a different training data patient in a plurality of training data patients and comprises a respective training input vector and a respective target output vector,

for each respective training dataset, the training input vector of the respective training dataset comprises a value for each element of the plurality of input elements,

for each respective training dataset, the target output vector of the respective training dataset comprises a value for each element of the plurality of output elements;

use the plurality of training datasets to train the NN;

obtain a current input vector that corresponds to a current patient;

apply the NN to the current input vector to generate a current output vector; and

determine, based on the current output vector, a classification of a shoulder condition of the current patient.

17 . The computing system of claim 16 , wherein:

a confidence value function outputs confidence values, wherein all possible confidence values output by the confidence value function are within a predefined range,

the one or more processors are configured such that, as part of applying the NN to the current input vector to generate the current output vector, the one or more processors, for each respective output layer neuron in the plurality of output layer neurons:

calculate an output value of the respective output layer neuron; and

apply the confidence value function with the output value of the respective output layer neuron as input to the confidence value function, wherein the confidence value function outputs a confidence value for the respective output layer neuron, and for each respective output layer neuron in the plurality of output layer neurons:

the output element corresponding to the respective output layer neuron specifies the confidence value for the respective output layer neuron, and

the confidence value for the respective output layer neuron is a measure of confidence that the shoulder condition of the current patient belongs to a class in the one or more shoulder pathology classification systems that corresponds to the output element corresponding to the respective output layer neuron.

18 . (canceled)

19 . . The computing system of claim 16 , wherein the computing system comprises a mixed reality (MR) visualization device that includes the one or more processors.

20 . (canceled)

21 . A computer-readable storage medium having instructions stored thereon that, when executed, configure a computing system to:

generate a plurality of training datasets from past shoulder surgery cases, wherein:

a neural network (NN) has an input layer, an output layer, and one or more hidden layers between the input layer and the output layer,

the input layer includes a plurality of input layer neurons, each input layer neuron in the plurality of input layer neurons corresponding to a different input element in a plurality of input elements,

the output layer includes a plurality of output layer neurons, each output layer neuron in the plurality of output layer neurons corresponding to a different output element in a plurality of output elements, each output element in the plurality of output elements corresponding to a different class in one or more shoulder pathology classification systems;

each respective training dataset corresponds to a different training data patient in a plurality of training data patients and comprises a respective training input vector and a respective target output vector,

for each respective training dataset, the training input vector of the respective training dataset comprises a value for each element of the plurality of input elements,

for each respective training dataset, the target output vector of the respective training dataset comprises a value for each element of the plurality of output elements;

using, by the computing system, the plurality of training datasets to train the NN;

obtain a current input vector that corresponds to a current patient;

apply the NN to the current input vector to generate a current output vector; and

determine, based on the current output vector, a classification of a shoulder condition of the current patient.