Methods, systems, apparatuses, and devices for facilitating a diagnosis of pathologies using a machine learning model

A system for facilitating a diagnosis of pathologies using at least one machine learning model, the system comprising:

• a communication connection configured for:
• receiving at least one medical data associated with at least one user from at least one device;
• and
• receiving at least one indication corresponding to at least one condition from the at least one device:
• a processing unit communicatively coupled with the communication connection, wherein the processing unit is configured for:
• identifying a plurality of clinician-annotated echocardiography reports and a plurality of echocardiography data associated with the plurality of clinician-annotated echocardiography reports from at least one data source based on the at least one indication;
• extracting at least one category from the plurality of clinician-annotated echocardiography reports using at least one natural language processing model;
• training one or more machine learning models using the at least one category and the plurality of echocardiography data;
• selecting the at least one machine learning model from the one or more machine learning models based on the at least one medical data;
• analyzing the at least one medical data using the at least one machine learning model, wherein the analyzing of the at least one medical data using the at least one machine learning model is further based on the selecting, wherein the at least one machine learning model comprises at least one artificial neural network comprising a plurality of layers, wherein the plurality of layers comprises at least one input layer, a plurality of output layers, and at least one middle layer between the at least one input layer and the plurality of output layers, wherein the at least one input layer is configured for taking a plurality of medical data as a plurality of inputs to the at least one input layer, wherein the at least one middle layer is configured for outputting a lower dimensional abstract vector space representation for each of the plurality of inputs by encoding the plurality of medical data to a lower dimensional abstract vector space, wherein the plurality of output layers is configured for classifying the lower dimensional abstract vector space representation to a plurality of outputs corresponding to a plurality of assessment parameters considered in the diagnosis of the pathologies, wherein each of the plurality of output layers is configured for communicating each of the plurality of outputs with at least one of the plurality of output layers, wherein each of the plurality of output layers is configured for adjusting each of the plurality of outputs based on the at least one of the plurality of outputs of at least one of the plurality of output layers, wherein the analyzing of the at least one medical data comprises inputting the at least one medical data to the at least one machine learning model;
• obtaining one or more outputs corresponding to one or more assessment parameters from the at least one machine learning model for the diagnosis of one or more pathologies based on the analyzing of the at least one medical data;
• and
• generating at least one result based on the one or more outputs;
• and
• a system memory communicatively coupled with the processing unit, wherein the system memory is configured for storing the at least one result and the at least one machine learning model.

A device for facilitating a diagnosis of pathologies using at least one machine learning model, the device comprising:

• at least one medical imaging unit configured for generating at least one medical data associated with at least one user by imaging at least one portion of at least one body part of the at least one user, wherein the at least one medical imaging unit is associated with at least one ultrasound imaging modality, wherein the imaging comprises ultrasound imaging of the at least one portion of the at least one body part of the at least one user, wherein the at least one medical imaging unit comprises at least one transducer, wherein the at least one transducer generates and transmits ultrasound waves to the at least one portion of the at least one body part and receives echos of the ultrasound waves from the at least one portion of the at least one body part for the generating of the at least one medical data;
• a processing unit communicatively coupled with the at least one medical imaging unit, wherein the processing unit is configured for:
• identifying a plurality of clinician-annotated echocardiography reports and a plurality of echocardiography data associated with the plurality of clinician-annotated echocardiography reports from at least one data source based on at least one indication corresponding to at least one condition;
• extracting at least one category from the plurality of clinician-annotated echocardiography reports using at least one natural language processing model;
• training one or more machine learning models using the at least one category and the plurality of echocardiography data;
• selecting the at least one machine learning model from the one or more machine learning models based on the at least one medical data;
• analyzing the at least one medical data using the at least one machine learning model, wherein the analyzing of the at least one medical data using the at least one machine learning model is further based on the selecting, wherein the at least one machine learning model comprises at least one artificial neural network comprising a plurality of layers, wherein the plurality of layers comprises at least one input layer, a plurality of output layers, and at least one middle layer between the at least one input layer and the plurality of output layers, wherein the at least one input layer is configured for taking a plurality of medical data as a plurality of inputs to the at least one input layer, wherein the at least one middle layer is configured for outputting a lower dimensional abstract vector space representation for each of the plurality of inputs by encoding the plurality of medical data to a lower dimensional abstract vector space, wherein the plurality of output layers is configured for classifying the lower dimensional abstract vector space representation to a plurality of outputs corresponding to a plurality of assessment parameters considered in the diagnosis of the pathologies, wherein each of the plurality of output layers is configured for communicating each of the plurality of outputs with at least one of the plurality of output layers, wherein each of the plurality of output layers is configured for adjusting each of the plurality of outputs based on the at least one of the plurality of outputs of at least one of the plurality of output layers, wherein the analyzing of the at least one medical data comprises inputting the at least one medical data to the at least one machine learning model;
• obtaining one or more outputs corresponding to one or more assessment parameters from the at least one machine learning model for the diagnosis of one or more pathologies based on the analyzing of the at least one medical data;
• and
• generating at least one result based on the one or more outputs;
• and
• a system memory communicatively coupled with the processing unit, wherein the system memory is configured for storing the at least one result and the at least one machine learning model.