SYSTEM AND ARCHITECTURE FOR PROCESSING ANATOMICAL THREE-DIMENSIONAL DATA

1 . A system comprising:

an application programming interface (API) gateway in wireless communication with a user equipment;

a cluster management component coupled to the API gateway, the cluster management component to perform orchestration and scaling operations on three-dimensional data of one or more features of a patient;

a processing component coupled to the cluster management component to perform operations on the three-dimensional data of the one or more features of the patient;

one or more machine learning models coupled to the cluster management component to perform operations on the three-dimensional data of the one or more features of the patient, the one or more machine learning models trained on image data associated with patients having various conditions, symptoms, cultural backgrounds, ages, genders, and physical characteristics together with symptom and treatment data associated with various ailments; and

a database coupled to the cluster management component for storing data associated with the system, the data including the three-dimensional data.

2 . The system of claim 1 , wherein the user equipment is a medical scanning device and the API gateway is configured to receive the three-dimensional data of the patient, meta data associated with the patient, and a healthcare professional request from the user equipment, the healthcare professional request including an operation to be performed by the system.

3 . The system of claim 1 , wherein the operations performed on the three-dimensional data is at least one of the following:

a landmark detection operation;

a metric determining operation;

a measurement determining operation;

a diagnostic operation;

an operation to determine a personalized therapy or treatment for the patient; or

an operation to determine a symptom or condition of the patient.

4 . The system of claim 1 , wherein:

the processing component is configured to perform the landmark detection operation, the metric determining operation, and the measurement determining operation; and

the machine learning models are configured to perform the diagnostic operation, the operation to determine the personalized therapy or treatment for the patient, and the operation to determine the symptom or condition of the patient.

5 . The system of claim 1 , further comprising:

a publish-subscribe component coupled to the cluster management component, the publish-subscribe component to set quotas and rate limits for healthcare professional requests by different users and prioritize incoming tasks; and

a broker component coupled to the publish-subscribe component, the broker component to manage messages within the system.

6 . The system of claim 5 , further comprising:

a cloud logging component coupled to the publish-subscribe component, the cloud logging component to generate log data associated with operations of the processing component and the one or more machine learned models;

a first task component coupled to the publish-subscribe component, the first task component to manage the operations of the processing component; and

a second task component coupled to the publish-subscribe component, the second task component to manage the operations of the one or more machine learning models.

7 . The system of claim 1 , wherein the database includes a first portion for storing personalized identifiable data associated with the patient and a second portion segmented from the first portion for storing non-personalized identifiable data.

8 . A method comprising:

capturing, via an application hosted on a user equipment, patient data and three-dimensional data of a feature of a patient;

processing, concurrently with the capturing and via a software development kit (SDK) associated with the user equipment, the three-dimensional data to generate a first mesh;

presenting, concurrently with the capturing and via the application and the user equipment, the first mesh as a first visualization;

transmitting the three-dimensional data and the patient data to an anatomical data processing system;

performing operations on the three-dimensional data to generate processed data associated with the patient;

transmitting the processed data to the SDK; and

presenting, via the application and the user equipment, a second visualization on the user equipment, the second visualization representing the feature of the patient.

9 . The method of claim 8 , wherein the first visualization includes at least one indicator, the at least one indicator to provide a user with a visual indication to guide the user in capturing additional three-dimensional data.

10 . The method of claim 8 , further comprising:

compressing the three-dimensional data prior to transmitting to the anatomical data processing system; and

encrypting the three-dimensional data prior to transmitting to the anatomical data processing system.

11 . The method of claim 8 , further comprising segregating, by the anatomical data processing system, the patient data into patient identifiable data and non-patient identifiable data.

12 . The method of claim 8 , wherein performing the operations on the three-dimensional data to generate processed data associated with the patient further comprises determining at least one of a landmark, a metric, or a measurement associated with the feature of the patient.

13 . The method of claim 8 , wherein performing the operations on the three-dimensional data to generate processed data associated with the patient further comprises inputting the three-dimensional data into one or more machine learning models trained on image data associated with patients having various conditions, symptoms, cultural backgrounds, ages, genders, and physical characteristics together with symptom and treatment data associated with various ailments, and receiving as an output of a diagnostic result, a personalized therapy or treatment for the patient, a symptom, or condition of the patient.

14 . (canceled)

15 . (canceled)

16 . One or more non-transitory computer-readable media storing instructions that, when executed by one or more processors, cause the one or more processors to:

capture, via an application hosted on a user equipment, patient data and three-dimensional data of a feature of a patient;

process, concurrently with the capturing and via a software development kit (SDK) associated with the user equipment, the three-dimensional data to generate a first mesh;

present, concurrently with the capturing and via the application and the user equipment, the first mesh as a first visualization;

transmit the three-dimensional data and the patient data to an anatomical data processing system;

perform operations on the three-dimensional data to generate processed data associated with the patient;

transmit the processed data to the SDK; and

present, via the application and the user equipment, a second visualization on the user equipment, the second visualization representing the feature of the patient.

17 . The one or more non-transitory computer-readable media of claim 16 , wherein the first visualization includes at least one indicator, the at least one indicator to provide a user with a visual indication to guide the user in capturing additional three-dimensional data.

18 . The one or more non-transitory computer-readable media of claim 16 , wherein the instructions when executed by the one or more processors, cause the one or more processors to:

compress the three-dimensional data prior to transmitting to the anatomical data processing system; and

encrypt the three-dimensional data prior to transmitting to the anatomical data processing system.

19 . The one or more non-transitory computer-readable media of claim 16 , wherein the instructions when executed by the one or more processors, cause the one or more processors to segregate the patient data into patient identifiable data and non-patient identifiable data.

20 . The one or more non-transitory computer-readable media of claim 16 , wherein performing the operations on the three-dimensional data to generate processed data associated with the patient further comprises determining at least one of a landmark, a metric, or a measurement associated with the feature of the patient.

21 . The one or more non-transitory computer-readable media of claim 16 , wherein performing the operations on the three-dimensional data to generate processed data associated with the patient further comprises inputting the three-dimensional data into one or more machine learning models trained on image data associated with patients having various conditions, symptoms, cultural backgrounds, ages, genders, and physical characteristics together with symptom and treatment data associated with various ailments, and receiving as an output of a diagnostic result, a personalized therapy or treatment for the patient, a symptom, or condition of the patient.

22 . The one or more non-transitory computer-readable media of claim 16 , wherein the instructions when executed by the one or more processors, cause the one or more processors to:

compress the processed data prior to transmitting to the SDK; and

encrypt the processed data prior to transmitting to the SDK.