System and method for diagnosing, monitoring, screening for or evaluating a musculoskeletal disease or condition

1. A computer-implemented method of diagnosing, monitoring, screening for or evaluating a musculoskeletal disease or condition, the method comprising:

quantifying one or more features segmented and identified from a medical image of a subject;

extracting non-image data pertaining to the subject and pertinent to the musculoskeletal disease or condition from one or more non-image data sources;

extracting clinically relevant features from the non-image data;

diagnosing, monitoring, screening for or evaluating the musculoskeletal disease or condition, comprising assessing the quantified features segmented from the medical image and the features extracted from the non-image data with a trained machine learning model trained to diagnose, monitor, screen for or evaluate one or more musculoskeletal diseases or conditions; and

outputting one or more results of the diagnosing, monitoring, screening for or evaluating the musculoskeletal disease or condition.

2. A method as claimed in claim 1 , including:

a) receiving the image and segmenting one or more features from the image, or

b) receiving the image with features segmented therefrom; and

identifying the features segmented from the image.

3. A method as claimed in claim 1 , wherein the segmenting and identifying are implemented with a machine learning algorithm trained segmentation and identification model, or segmentation and identification model comprising a deep convolutional neural network trained model, configured to segment and identify the features from the image.

4. A method as claimed in claim 1 , wherein the trained machine learning model

(a) is a disease classification model;

(b) is a model trained using features extracted from patient data and labels or annotations indicating disease or non-disease; and/or

(c) comprises a deep learning neural network or other machine learning algorithms.

5. A method as claimed in claim 1 , further comprising (i) training the trained machine learning model, and/or (ii) updating the trained machine learning model with additional labelled data derived from new or newly analysed subject data.

6. A method as claimed in claim 1 , wherein the results comprise (i) one or more disease classifications; (ii) one or more disease probabilities; and/or (iii) one or more fracture risk scores.

7. A system for diagnosing, monitoring, screening for or evaluating a musculoskeletal disease or condition, the system comprising:

a feature quantifier configured to quantify one or more features segmented and identified from a medical image of a subject;

a non-image data processor configured to extract non-image data pertaining to the subject and pertinent to the musculoskeletal disease or condition from one or more non-image data sources and to extract clinically relevant features from the non-image data;

a feature assessor configured to diagnose, monitor, screen for or evaluate the musculoskeletal disease or condition, comprising assessing the quantified features segmented from the medical image and the features extracted from the non-image data with a trained machine learning model trained to diagnose, monitor, screen for or evaluate one or more musculoskeletal diseases or conditions; and

an output configured to output one or more results of diagnosing, monitoring, screening for or evaluating the musculoskeletal disease or condition.

8. A system as claimed in claim 7 , further comprising a segmenter and identifier configured to receive the image, segment one or more features from the image, and identify the features segmented from the image.

9. A system as claimed in claim 8 , wherein the segmenter and identifier comprises a machine learning algorithm trained segmentation and identification model, or segmentation and identification model comprising a deep convolutional neural network trained model, configured to segment and identify the features from the image.

10. A system as claimed in claim 7 , wherein the trained machine learning model

(a) is a disease classification model;

(b) is a model trained using features extracted from patient data and labels or annotations indicating disease or non-disease; and/or

(c) comprises a deep learning neural network or other machine learning algorithms.

11. A system as claimed in claim 7 , further comprising a machine learning model trainer configured to update the trained machine learning model with additional labelled data derived from new or newly analysed subject data.

12. A system as claimed in claim 7 , wherein the results comprise (i) one or more disease classifications; (ii) one or more disease probabilities; and/or (iii) one or more fracture risk scores.

13. A non-transitory computer-readable medium comprising computer program code, wherein the computer program code comprises instructions configured, when executed by one or more computing devices, to implement the method of claim 1 .

14. A method as claimed in claim 1 , wherein the disease or condition is arthritis, bone fracture, or osteomalacia.

15. A system as claimed in claim 7 , wherein the disease or condition is arthritis, bone fracture, or osteomalacia.

16. A computer-implemented method of determining a treatment, or producing guidelines for treatment of, a musculoskeletal disease or condition, the method comprising

quantifying one or more features segmented and identified from a medical image of a subject;

extracting non-image data pertaining to the subject and pertinent to the musculoskeletal disease or condition from one or more non-image data sources;

extracting clinically relevant features from the non-image data;

determining a treatment, or producing guidelines for treatment of, the musculoskeletal disease or condition, comprising assessing the quantified features segmented from the medical image and the features extracted from the non-image data with a trained machine learning model trained to determine a treatment, or produce guidelines for treatment of, one or more musculoskeletal diseases or conditions; and

outputting one or more results of the determining a treatment, or producing guidelines for treatment of, the musculoskeletal disease or condition.

17. A method as claimed in claim 16 , wherein the disease or condition is arthritis, bone fracture, or osteomalacia.

18. A non-transitory computer-readable medium comprising computer program code, wherein the computer program code comprises instructions configured, when executed by one or more computing devices, to implement the method of claim 16 .

19. A system for determining a treatment, or producing guidelines for treatment of, a musculoskeletal disease or condition, the system comprising:

a feature quantifier configured to quantify one or more features segmented and identified from a medical image of a subject;

a non-image data processor configured to extract non-image data pertaining to the subject and pertinent to the musculoskeletal disease or condition from one or more non-image data sources and to extract clinically relevant features from the non-image data;

a feature assessor configured to determine a treatment, or produce guidelines for treatment of, the musculoskeletal disease or condition, comprising assessing the quantified features segmented from the medical image and the features extracted from the non-image data with a trained machine learning model trained to determine a treatment, or produce guidelines for treatment of, one or more musculoskeletal diseases or conditions; and

an output configured to output one or more results of the determining a treatment, or producing guidelines for treatment of, the musculoskeletal disease or condition.

20. A system as claimed in claim 19 , wherein the disease or condition is arthritis, bone fracture, or osteomalacia.