Methods of implementing an artificial intelligence based imaging platform for precision medical treatment predictive modeling platforms

1. A method of implementing an artificial intelligence based imaging platform for cardiopulmonary analysis comprising the steps of:

Providing a multilayer convolutional network for cardiopulmonary analysis configured for segmenting data sets of cardiopulmonary scans into resolution voxels, wherein the resolution voxels are representative of uniform volumetric regions and define the smallest volumetric region within which the platform can segment data sets;

Defining classification voxels which are representative of uniform volumetric regions wherein each classification voxel is made up of one or more resolution voxels;

Supervised learning of the platform by classification of tissue within said classification voxels of a specific given training data set by the multilayer convolutional network for cardiopulmonary analysis with each classification voxel of the training data set having a predetermined ground truth;

Validating the classification of tissue within said classification voxels of a specific given validation data set by the multilayer convolutional network for cardiopulmonary analysis with each classification voxel of the validation data set having a predetermined ground truth;

Implementing the platform by classification of cardiopulmonary tissue within classification voxels of a specific given patient data sets by the multilayer convolutional network for cardiopulmonary analysis with each classification voxel of each data set assigned a label, wherein the Implementing the platform by classification of cardiopulmonary tissue within classification voxels of a specific given patient data sets by the multilayer convolutional network for cardiopulmonary analysis further includes prediction of transcatheter aortic valve implantation results for the patient.

2. The method of implementing an artificial intelligence based imaging platform for cardiopulmonary analysis according to claim 1 , wherein the platform further includes inputting of genetic background for the patients.

3. The method of implementing an artificial intelligence based imaging platform for cardiopulmonary analysis according to claim 1 , wherein the cardiopulmonary scans include MRI scans.

4. The method of implementing an artificial intelligence based imaging platform for cardiopulmonary analysis according to claim 3 wherein the classification voxels are at least 20 times larger than the resolution voxels.

5. The method of implementing an artificial intelligence based imaging platform for cardiopulmonary analysis according to claim 3 wherein the classification voxels are at least 10 times larger than the resolution voxels.

6. The method of implementing an artificial intelligence based imaging platform for cardiopulmonary analysis according to claim 3 wherein the classification voxels are at least 5 times larger than the resolution voxels.

7. The method of implementing an artificial intelligence based imaging platform for cardiopulmonary analysis according to claim 6 resolution voxels are cubes of 1 mm.

8. The method of implementing an artificial intelligence based imaging platform for cardiopulmonary analysis according to claim 3 wherein the specific given training data set includes at least 40 full scans.

9. The method of implementing an artificial intelligence based imaging platform for cardiopulmonary analysis according to claim 3 wherein the specific given training data set includes at least 60 full scans.

10. The method of implementing an artificial intelligence based imaging platform for cardiopulmonary analysis according to claim 3 wherein the specific given training data set includes at least 100 full scans.

11. The method of implementing an artificial intelligence based imaging platform for cardiopulmonary analysis according to claim 3 wherein the validation data set includes less full scans than the training data set.

12. The method of implementing an artificial intelligence based neuroradiology platform for neurological tumor identification according to claim 3 wherein the validating step results in a standard measuring metric.

13. The method of implementing an artificial intelligence based imaging platform for cardiopulmonary analysis according to claim 12 wherein the standard measuring metric includes one of a Sørensen-Dice coefficient or a Hausdorff distance.

14. The method of implementing an artificial intelligence based imaging platform for cardiopulmonary analysis according to claim 12 wherein the standard measuring metric includes a mean Sørensen-Dice coefficient of at least 0.60.

15. The method of implementing an artificial intelligence based imaging platform for cardiopulmonary analysis according to claim 12 wherein the standard measuring metric includes a mean Hausdorff distance less than −1.50.

16. The method of implementing an artificial intelligence based imaging platform for cardiopulmonary analysis according to claim 12 further including the step of resetting the size of the classification voxel following supervised learning, wherein the size of the classification voxel used in the validating step is smaller than the size of the classification voxel used in supervised learning.

17. The method of implementing an artificial intelligence based imaging platform for cardiopulmonary analysis according to claim 12 further including the step of resetting the size of the classification voxel following validation, wherein size of the classification voxel used in the validating step is equal to or smaller than the classification voxel size used by the platform in implementation.

18. The method of implementing an artificial intelligence based imaging platform for cardiopulmonary analysis according to claim 3 wherein the platform identifies valve leaflet and valve sizes.