Abstract: A method of estimating gestational age (GA) is shown generally at 3000. At Step 3010 an ultrasound image is acquired. The image is fed into the model at Step 3020 and a predicted GA value, together with an associated confidence interval are calculated at Step 3030. At Step 3040 it is determined whether the confidence interval is narrower than a value for the best confidence interval obtained. If not, the ultrasound operation is optionally directed to acquire an ultrasound image of a particular plane in the fetus at Step 3050. On the other hand, if the value is determined to be narrower than the best obtained at Step 3040, the predicted value and confidence interval are displayed at Step 3060 and a register for the value of the best confidence interval is updated at Step 3070.

Abstract: Methods of training a machine learning model and using the trained model to assess ultrasound measurement data are disclosed. In one arrangement, training data comprises a plurality of classified frames of ultrasound measurement data. Each of at least a subset of the classified frames is classified as representing an imaging plane capable of providing information about a respective target anatomical feature. First and second samples of frames are selected. A machine learning model derives prototype feature vectors from the first sample and feature vectors from the second sample. A loss function depending on metrics representing distances between the feature vectors and the prototype feature vectors is optimized to train the machine learning model.

Abstract: A system and method are provided to automatically categorize biological and medical images. The new system and method can incorporate a machine learning classifier in which novel ideas are provided to guide the classifier to focus on regions of interest (ROI) within medical images for categorizing or classifying the images. The system and method can ignore regions when misleading structures exist. The detection and classification of one or more features of interest within a discriminative region of interest within an image are rendered invariant to differences in translation, orientation and/or scaling of the one or more features of interest within the medical image(s). The system and method allow a processor to more quickly, efficiently and accurately process and categorize medical images.

Abstract: A system and method are provided to automatically categorize biological and medical images. The new system and method can incorporate a machine learning classifier in which novel ideas are provided to guide the classifier to focus on regions of interest (ROI) within medical images for categorizing or classifying the images. The system and method can ignore regions when misleading structures exist. The detection and classification of one or more features of interest within a discriminative region of interest within an image are rendered invariant to differences in translation, orientation and/or scaling of the one or more features of interest within the medical image(s). The system and method allow a processor to more quickly, efficiently and accurately process and categorize medical images.