Automated ultrasound video interpretation of a body part with one or more convolutional neural networks

A system, comprising:

• an ultrasound transducer configured to acquire an ultrasound video having a plurality of images of a body portion, wherein the images of the body portion includes an image of a lung;
• and
• an electronic circuit coupled to the ultrasound transducer configured
• to execute a neural network;
• to detect at least one feature in each of the images while executing the neural network;
• to determine a respective position and a respective class of each of the detected at least one feature while executing the neural network, determining a respective position including determining a respective bounding box in which the detected at least one feature is disposed and determining a respective class including determining a respective probability that the detected at least one feature belongs to the respective class;
• to group the detected at least one feature in at least one cluster in response to the respective position and the respective class while executing the neural network, each cluster corresponding to a respective actual feature;
• to determine a grade for the respective actual feature in response to the cluster in which the respective actual feature is grouped while executing the neural network;
• and
• to diagnose a pathology of the lung in response to the respective determined class of each of the detected at least one feature;
• wherein each detection of the at least one feature is represented by an output having the respective probability, a respective coordinate and a respective width of the respective bounding box.

A tangible, non-transitory computer-readable medium storing instructions that, when executed by a computing circuit, cause the computing circuit, or another circuit under control of the computing circuit, to execute a neural network:

• to detect at least one feature in each of images of a body portion acquired by an ultrasound transducer, wherein the images of the body portion includes an image of a lung;
• and
• to determine a respective position and a respective class of each of the detected at least one feature, determining a respective position including determining a respective bounding box in which the detected at least one feature is disposed and determining a respective class including determining a respective probability that the detected at least one feature belongs to the respective class;
• to group the detected at least one feature in at least one cluster in response to the respective position and the respective class, each cluster corresponding to a respective actual feature;
• and
• to determine a grade for the respective actual feature in response to the cluster in which the respective actual feature is grouped;
• and
• to diagnose a pathology of the lung in response to the respective determined class of each of the detected at least one feature;
• wherein each detection of the at least one feature is represented by an output having the respective probability, a respective coordinate and a respective width of the respective bounding box.