Method for developing a machine learning model of a neural network for classifying medical images

A method for developing a machine learning model of a neural network for classifying images for a medical procedure using an ultrasound system, the method comprising:

• A) performing a first medical procedure using an ultrasound system;
• B) automatically capturing a plurality of ultrasound images during the performance of the first medical procedure, wherein each of the plurality of ultrasound images is captured at a defined sampling rate according to defined image capture criteria;
• C) providing a plurality of feature modules, wherein each feature module defines a feature which may be present in an image captured during the medical procedure;
• D) automatically analyzing each image using the plurality of feature modules;
• E) automatically determining, for each image, whether or not each of the plurality of features is present in the image, based on the analysis of each imagine using the feature modules;
• F) automatically labeling each image as belonging to one class of a plurality of image classes associated with the medical procedure;
• G) automatically splitting the plurality of images into a training set of images and a validation set of images;
• H) providing a deep machine learning (DML) platform having a neural network to be trained loaded thereon, the DML platform having a plurality of adjustable parameters for controlling the outcome of a training process;
• I) feeding the training set of images into the DML platform;
• J) performing the training process for the neural network to generate a machine learning model of the neural network;
• K) obtaining training process metrics of the ability of the generated machine learning model to classify images during the training process, wherein the training process metrics comprise at least one of a loss metric, an accuracy metric, and an error metric for the training process;
• L) determining whether each of the at least one training process metrics is within an acceptable threshold for the training process metric;
• M) if one or more of the training process metrics are not within an acceptable threshold, adjusting one or more of the plurality of adjustable DML parameters and repeating steps J, K, and L using the training set of images;
• N) if each of the training process metrics is within an acceptable threshold for the metric, performing a validation process using the validation set of images;
• O) obtaining validation process metrics of the ability of the generated machine learning model to classify images during the validation process, wherein the validation process metrics comprise at least one of a loss metric, an accuracy metric, and an error metric for the validation process;
• P) determining whether each of the validation process metrics is within an acceptable threshold for the validation process metric;
• Q) if one or more of the validation process metrics are not within an acceptable threshold, adjusting one or more of the plurality of adjustable DML parameters and repeating steps J-P;
• and
• R) if each of the validation process metrics is within an acceptable threshold for each metric, storing the machine learning model for the neural network.