Abstract: Embodiments of the disclosure provide for improved processing of data with different timescales, for example high-frequency data and low-frequency data. Embodiments specifically improve such processing of different timescale data processed by a machine learning model. Additionally or alternatively, some embodiments include improved processing of data with different timescales by selecting an optimal variant from a plurality of possible variants of a prediction model.

Abstract: Embodiments herein describe systems and methods for automated clinical image quality assessment and uses thereof are provided. Many embodiments provide instructions and feedback for clinically relevant image quality. Several embodiments provide methods to generate a model to assess image quality.

Abstract: The present disclosure provides methods and systems for predicting future disease states of a subject. An exemplary method comprises: (a) obtaining an image from an imaging device, wherein the image indicates a disease associated with the subject; (b) generating an annotation mask for a current disease state; (c) processing the image data and the annotation mask to generate a temporal sequence of images with corresponding predicted disease states; and (d) outputting the temporal sequence of images and disease analytics within a graphical user interface (GUI).