Device at the point of imaging for integrating training of ai algorithms into the clinical workflow

Abstract

The present invention relates to image processing. In order to facilitate a sustainable infrastructure for training of AI algorithms, an imaging system is proposed with a mobile annotation device to receive an image acquired by the medical imaging apparatus (e.g. x-ray, CT or MRI scanner) in real-time, that is, during the imaging session. The image acquired by the medical imaging apparatus is then displayed, thereby allowing the user to annotate the acquired image. The user annotation may comprise one or more of a recommended workflow in relation to the patient, an indication of an image quality in relation to the first image, an indication on a medical finding, a priority information representing the urgency of the medical finding. The acquired image and the user annotation are then stored in a database, thereby creating a training database for training of the AI algorithm. Alternatively or additionally, the user interface may receive a user annotation in relation to the set of pre-image settings used by the medical imaging apparatus for acquiring the image of the patient. In an example of x-ray chest imaging, the user annotation could be collimation settings, the exposure time settings, the tube voltage settings, the focal spot size settings, the selection of the X-ray sensitive areas for an X-ray imaging system to apply the correct dose to the patient, etc. The set of pre-image settings and the user annotation are then stored in a training database, thereby creating a training database for training of the AI algorithm. In this way, the images and the sets of pre-image settings can be directly chosen from the clinical workflow and there is no need to select the images and/or the sets of pre-image settings and transfer them somewhere else (e.g. from other facilities) for the development. Accordingly, the parameters of the AI algorithm, which has been trained using the training data from the training database, are adapted to fit the needs and standards of a particular facility, which makes it possible to obtain a sustainable architecture that can be used to train AI algorithms for different applications based on the customer's needs.