Abstract: Various methods and systems are provided for a medical system, comprising a client device having a graphical user interface (GUI) and a display device, the client device operably coupled to a network, and an analytics tool configured with instructions stored on a memory and executable by a processor to receive a first user input via the client device, identify values of at least one variable of interest (VOI) from a subject of a benchmark target variable which differ by at least a first threshold amount from other values of the same VOI from other subjects of the benchmark target variable within a benchmarking context, generate and output for display on the display device a causal graph illustrating influences of different study variables on an identified VOI value, and automatically update the causal graph and display thereof based on a second user input to adjust at least one study variable.

Abstract: A system and method for medical imaging protocol name standardization includes generating a synthetic training dataset from medical standards in public documentation utilizing knowledge elicitation, wherein the synthetic training dataset includes, for a given language and a given imaging modality, a plurality of combinations of possible medical imaging protocol names for respective standard protocol codes of a plurality of standard protocol codes for each standard medical imaging protocol. The system and method also includes generating a lightweight text classification model from the synthetic training dataset utilizing machine learning. The system and method further includes utilizing the lightweight text classification model to receive a medical imaging protocol name and to output a list of most probable protocol codes based on the medical imaging protocol name.

Abstract: Methods and systems are provided for automatically evaluating an image quality of medical images, and collecting user feedback with respect to the image quality via a graphical user interface (GUI). The tool provides an assessment of noise, contrast, and other characteristics of slices of an image associated with organs and/or anatomical structures of the image based on various low-level metrics, and calculates an overall image quality score of the image by aggregating the image scores of the organs and/or anatomical structures. The score may be estimated by a machine learning (ML) model based on the low-level metrics. The user can provide feedback with respect to the image quality score, which may be used to increase an accuracy and/or performance of the ML model. In this way, feedback may be efficiently collected and used to refine the ML model, leading to more accurate scan protocols and higher quality CT images.

Abstract: A system and method for determining a data quality of DICOM data are provided. Information related to a data ingestion of the DICOM data from a radiology device to a database may be received. Respective data qualities of the DICOM data at the set of data ingestion stages of the data ingestion may be determined using a set of data validation rules corresponding to a set of data ingestion stages of the data ingestion of the DICOM data from the radiology device to the database. A user interface that displays information related to the respective data qualities of the DICOM data at the set of data ingestion stages of the data ingestion may be generated.

Abstract: Various methods and systems are provided for a medical system, comprising a client device having a graphical user interface (GUI) and a display device, the client device operably coupled to a network, and an analytics tool configured with instructions stored on a memory and executable by a processor to receive a first user input via the client device, identify values of at least one variable of interest (VOI) from a subject of a benchmark target variable which differ by at least a first threshold amount from other values of the same VOI from other subjects of the benchmark target variable within a benchmarking context, generate and output for display on the display device a causal graph illustrating influences of different study variables on an identified VOI value, and automatically update the causal graph and display thereof based on a second user input to adjust at least one study variable.

Abstract: Methods of and systems for establishing an augmented communication channel between an interactive voice response (IVR) system and an electronic device associated with a user are disclosed. A first method comprises sending, an audio call request; establishing an audio channel; establishing a data channel between the IVR system and the electronic device; receiving, via the data channel, IVR tree data, the IVR tree data allowing association of a position of the user with respect to the menu navigation structure; sending, via one of the audio channel and the data channel, a user selection relating to the IVR tree data; and receiving, via the other one of the audio channel and the data channel, an updated position of the user with respect to the menu navigation structure based on the user selection. A method to be executed by the IVR system is also disclosed.