Prediction of candidates for spinal neuromodulation
Described herein are various implementations of systems and methods for determining likelihood of a patient favorably responding to a neuromodulation procedure based on a quantitative or objective score or determination based on a plurality of indicators of pain (e.g., chronic low back pain stemming from one or more vertebral bodies or vertebral endplates of a patient). The systems and methods may involve application of artificial intelligence techniques (e.g., trained algorithms, machine learning or deep learning algorithms, and/or trained neural networks).
1. A computer-implemented method of quantitatively predicting likelihood that a particular subject would respond favorably to basivertebral nerve ablation to treat back pain, the computer-implemented method comprising:
receiving one or more magnetic resonance images (MRIs) of at least a portion of a spine of the particular subject;
applying pre-processing imaging techniques to the one or more MRIs;
extracting features from the one or more MRIs to identify a plurality of indicators of back pain, wherein the extracting features is performed by a processor executing instructions stored in memory of an MRI apparatus,
wherein the plurality of indicators comprises at least one of: (i) bone marrow intensity changes and (ii) vertebral endplate defects or characteristics of vertebral endplate degeneration;
determining an objective score indicative of a likelihood that the particular subject would respond favorably to basivertebral nerve ablation based on said extracting using the processor; and
transmitting the objective score to a display device associated with the MRI apparatus.
2. The computer-implemented method of claim 1 , further comprising applying one or more rules on the extracted features to generate a confidence level.
3. The computer-implemented method of claim 2 wherein the one or more rules are based on one or more additional indicators.
4. The computer-implemented method of claim 3 , wherein the one or more additional indicators comprise one or more of the following:
changes in multifidus muscle characteristics;
bone turnover in single-photon emission computed tomography images; and
a pain score obtained for the particular subject.
5. The computer-implemented method of claim 1 , further comprising quantifying the identified plurality of indicators of back pain, wherein the quantifying the identified plurality of indicators comprises one or more of:
determining a quantity of the bone marrow intensity changes or vertebral endplate defects;
determining a level of extent of the bone marrow intensity changes or vertebral endplate defects; and
quantifying identified fat fraction changes.
6. The computer-implemented method of claim 1 , wherein the back pain is chronic low back pain.
7. The computer-implemented method of claim 1 , wherein at least some of the MRIs comprise T1-weighted MRIs, T2 weighted MRIs or fat suppression MRIs.
8. The computer-implemented method of claim 1 , wherein at least some of the MRIs are generated using ultrashort time-to-echo MRI techniques or least-squares estimation MRI techniques.
9. The computer-implemented method of claim 1 , wherein the extracting features from the one or more MRIs to identify the plurality of indicators of back pain comprises applying a trained neural network to the one or more MRIs to automatically identify the plurality of indicators of back pain.
10. The computer-implemented method of claim 1 , wherein the extracting features from the one or more MRIs to identify the plurality of indicators of back pain comprises one or more of:
identifying irregularities or deviations to a normal continuous lining of the vertebral endplate;
identifying deviations from a normal contour profile of a vertebral endplate;
identifying fat fraction changes; and
identifying one or more phenotype subtypes of vertebral endplate defects.
11. The computer-implemented method of claim 1 , further comprising displaying an output of the objective score on a display.
12. The computer-implemented method of claim 1 , wherein the plurality of indicators comprises both (i) bone marrow intensity changes and (ii) vertebral endplate defects or characteristics of vertebral endplate degeneration.
13. The computer-implemented method of claim 1 , wherein the objective score is used to generate a recommendation for treatment.
14. A computer-implemented method of quantitatively predicting likelihood that a particular subject would respond favorably to basivertebral nerve ablation to treat back pain, the method comprising:
receiving one or more images of at least a portion of a spine of the particular subject;
applying pre-processing imaging techniques to the one or more images;
extracting features from the one or more images to identify a plurality of indicators of back pain, wherein the extracting features is performed by a processor executing instructions stored in memory of an MRI apparatus,
wherein the plurality of indicators comprises at least one of: (i) bone marrow intensity changes and (ii) vertebral endplate defects or characteristics of vertebral endplate degeneration;
determining an objective score indicative of a likelihood that the particular subject would respond favorably to basivertebral nerve ablation based on said extracting using the processor; and
transmitting the objective score to a display device associated with the MRI apparatus.
15. The computer-implemented method of claim 14 , further comprising quantifying the identified plurality of indicators of back pain, wherein the quantifying the identified plurality of indicators comprises one or more of:
determining a quantity of the bone marrow intensity changes or vertebral endplate defects;
determining a level of extent of the bone marrow intensity changes or vertebral endplate defects; and
quantifying identified fat fraction changes.
16. The computer-implemented method of claim 14 , further comprising applying one or more rules on the extracted features to generate a confidence level, wherein the one or more rules are based on one or more additional indicators.
17. The computer-implemented method of claim 16 , wherein the one or more additional indicators comprise one or more of the following:
changes in multifidus muscle characteristics;
bone turnover in single-photon emission computed tomography images; and
a pain score obtained for the particular subject.
18. The computer-implemented method of claim 14 , further comprising displaying an output of the objective score on a display.
19. The computer-implemented method of claim 14 , wherein the extracting features from the one or more images to identify the plurality of indicators of back pain comprises applying a trained neural network to the one or more images to automatically identify the plurality of indicators of back pain.
20. The computer-implemented method of claim 14 , wherein the one or more images comprise one or more of: magnetic resonance images, computed tomography images, X-ray images, and single-photon emission computed tomography images.
21. A computer-implemented method of quantitatively predicting likelihood that a particular subject would respond favorably to basivertebral nerve ablation to treat back pain, the method comprising:
receiving one or more images of at least a portion of a spine of the particular subject;
applying pre-processing imaging techniques to the one or more images;
extracting features from the one or more images to identify a plurality of indicators of back pain, wherein the extracting features is performed by a processor executing instructions stored in memory of an MRI apparatus,
wherein the plurality of indicators comprises at least one of: (i) bone marrow intensity changes and (ii) vertebral endplate defects or characteristics of vertebral endplate degeneration;
determining an objective score indicative of a likelihood that the particular subject would respond favorably to basivertebral nerve ablation based on said extracting using the processor;
transmitting the objective score to a display device associated with the MRI apparatus;
displaying the objective score on the display device; and
recommending a treatment to be performed.