Methods and systems for identifying a heart condition in a non-human subject using predictive models

A server comprising:

• one or more processors;
• and
• non-transitory computer readable medium having stored therein instructions that when executed by the one or more processors, causes the server to perform functions comprising:
• receiving a first medical image and a second medical image of a non-human subject;
• determining, by the one or more processors executing a first machine-learning logic and based on the first medical image and the second medical image, a first dimensional feature and a second dimensional feature of a heart of the non-human subject, wherein the first machine-learning logic is trained using medical image training data labeled with anatomical landmarks,
• wherein the first machine-learning logic programmatically identifies a first plurality of pixel points corresponding to the anatomical landmarks in the first medical image and wherein determining the first dimensional feature comprises calculating a vertebral heart scale (VHS) by performing measurements using the first plurality of pixel points of a longest axis of a cardiac silhouette and a short axis of the cardiac silhouette perpendicular to the longest axis, and programmatically transferring the measurements to a vertebrae count;
• wherein the first machine-learning logic programmatically identifies a second plurality of pixel points corresponding to the anatomical landmarks in the second medical image and wherein determining the second dimensional feature comprises identifying from the second plurality of pixel points measurements of the heart across different phases of a cardiac cycle, including at least an end-diastole phase measurement and an end-systole phase measurement;
• receiving medical information associated with the non-human subject on a graphical user interface, the medical information including one or more of a species, an age, a weight, an output of a brain natriuretic peptide (BNP) test, and a murmur observation;
• determining, by the one or more processors executing a second machine-learning logic and based on the medical information and the first dimensional feature and the second dimensional feature of the heart, a likelihood of a heart disease, wherein the second machine-learning logic is trained using heart disease training data and combinations of which are labeled with different stages of heart disease;
• and
• displaying the likelihood of the heart disease on the graphical user interface.

A non-transitory computer readable medium having stored thereon instructions, that when executed by one or more processors of a computing device, cause the computing device to perform functions comprising:

• receiving a first medical image and a second medical image of a non-human subject;
• determining, by the one or more processors executing a first machine-learning logic and based on the first medical image and the second medical image, a first dimensional feature and a second dimensional feature of a heart of the non-human subject, wherein the first machine-learning logic is trained using medical image training data labeled with anatomical landmarks,
• wherein the first machine-learning logic programmatically identifies a first plurality of pixel points corresponding to the anatomical landmarks in the first medical image and wherein determining the first dimensional feature comprises calculating a vertebral heart scale (VHS) by performing measurements using the first plurality of pixel points of a longest axis of a cardiac silhouette and a short axis of the cardiac silhouette perpendicular to the longest axis, and programmatically transferring the measurements to a vertebrae count;
• wherein the first machine-learning logic programmatically identifies a second plurality of pixel points corresponding to the anatomical landmarks in the second medical image and wherein determining the second dimensional feature comprises identifying from the second plurality of pixel points measurements of the heart across different phases of a cardiac cycle, including at least an end-diastole phase measurement and an end-systole phase measurement;
• receiving medical information associated with the non-human subject on a graphical user interface, the medical information including one or more of a species, an age, a weight, an output of a brain natriuretic peptide (BNP) test, and a murmur observation;
• determining, by the one or more processors executing a second machine-learning logic and based on the medical information and the first dimensional feature and the second dimensional feature of the heart, a likelihood of a heart disease, wherein the second machine-learning logic is trained using heart disease training data and combinations of which are labeled with different stages of heart disease;
• and
• displaying the likelihood of the heart disease on the graphical user interface.