A non-invasive method for cardiac arrhythmia risk stratification may include identifying, based at least on an electrical recording of a patient, a cardiac depolarization simulation and a cardiac repolarization simulation corresponding to an electrical recording of a patient. One or more regions of increased spatial repolarization gradient in which a first area of a myocardium of the patient exhibits a first repolarization rate that differs from a second repolarization rate of a second area of the myocardium by an amount then divided by the spatial distance between the two regions, by a threshold value may be determined based on the cardiac depolarization simulation and the cardiac repolarization simulation. A risk of cardiac arrhythmia for the patient may be determined based a magnitude of the increased spatial repolarization gradient. Moreover, a treatment plan for the patient may be determined based on the magnitude and/or location of the increased spatial repolarization gradient.

A non-invasive method for cardiac arrhythmia risk stratification may include identifying, based at least on an electrical recording of a patient, a cardiac depolarization simulation and a cardiac repolarization simulation corresponding to an electrical recording of a patient. One or more regions of increased spatial repolarization gradient in which a first area of a myocardium of the patient exhibits a first repolarization rate that differs from a second repolarization rate of a second area of the myocardium by an amount then divided by the spatial distance between the two regions, by a threshold value may be determined based on the cardiac depolarization simulation and the cardiac repolarization simulation. A risk of cardiac arrhythmia for the patient may be determined based a magnitude of the increased spatial repolarization gradient. Moreover, a treatment plan for the patient may be determined based on the magnitude and/or location of the increased spatial repolarization gradient.

Described is an injector system for implementing a split bolus injection procedure. The injector system includes a processor and a non-transitory storage medium having programming instructions stored therein that, when executed by the processor, enable the injector system to operate as a parameter generation system for use in determining parameters associated with a split bolus injection protocol via which injection of the contrast agent by the injector system is controlled. The split bolus injection protocol includes at least a loading injection and a diagnostic injection, wherein the loading injection is performed before the diagnostic injection, and wherein a pause separates the loading injection from the diagnostic injection. Also described is a method for patient imaging using a split bolus injection technique and a system having an imaging device and the injector system described above.

Described is an injector system for implementing a split bolus injection procedure. The injector system includes a processor and a non-transitory storage medium having programming instructions stored therein that, when executed by the processor, enable the injector system to operate as a parameter generation system for use in determining parameters associated with a split bolus injection protocol via which injection of the contrast agent by the injector system is controlled. The split bolus injection protocol includes at least a loading injection and a diagnostic injection, wherein the loading injection is performed before the diagnostic injection, and wherein a pause separates the loading injection from the diagnostic injection. Also described is a method for patient imaging using a split bolus injection technique and a system having an imaging device and the injector system described above.

A system and method may be provided to predict a value of a field of interest about a patient procedure. Data may be received from a health provider. A statistical model or machine learning model may be built based on the data in order to predict the value of the field of interest. In some embodiments, a plurality of models are used to predict different aspects of the procedure and are combined by a main model.

A mechanism is provided for implementing a dynamic context-based collaborative medical concept interpreter for automatically generating and presenting summarized explanations of medical concepts. The dynamic context-based collaborative medical concept interpreter performs natural language processing on a real-time patient-provider communication to identify one or more medical concepts referred to in the communication. The dynamic context-based collaborative medical concept interpreter adjusts one or more previous explanations for the one or more medical concepts referred to in the communication using a set of contextual factors. The dynamic context-based collaborative medical concept interpreter generates an abstractive summary that summarizes ranked explanations of the one or more medical concepts based on an original language used in the one or more previous explanations. The dynamic context-based collaborative medical concept interpreter presents, in real time, the abstractive summaries of the one or more medical concepts to the patient and the provider in real-time patient-provider communication.

Certain aspects of the present disclosure provide techniques for wound management and treatment. This includes determining characteristics of a wound for a patient based on an image of the wound, including detecting the characteristics based on analyzing the image using a first ML model trained to detect wound characteristics from a captured image. The techniques further include identifying patient medical data including characteristics relating to a medical history for the patient, and predicting a first care plan for the patient based on providing the characteristics of the wound and the patient medical data to a second ML model. The second ML model is trained to predict the first care plan using prior wound care outcome data including a prior wound care outcomes relating to prior patients. The first care plan is configured to be used to treat the wound for the patient.

Systems, methods, and instrumentalities are disclosed for switching a control scheme to control a set of system modules and/or modular devices of a surgical hub. A surgical hub may determine a first control scheme that is configured to control a set of system modules and/or modular devices. The surgical hub may receive an input from one of the set of modules or a device located in an OR. The surgical hub may make a determination that at least one of a safety status level or an overload status level of the surgical hub is higher than its threshold value. Based on at least the received input and the determination, the surgical hub may determine a second control scheme to be used to control the set of system modules. The surgical hub may send a control program indicating the second control scheme to one or more system modules and/or modular devices.

A method for imaging expected results of a medical cosmetic treatment includes converting an input image of an anatomical feature into an input image vector. A direction vector corresponding to the medical cosmetic treatment is determined. An amplitude of the direction vector is determined. The direction vector is multiplied by the determined amplitude to obtain a product vector. The product vector is vector added to the input image vector. An output image corresponding to the expected visual appearance of the human anatomical feature is generated from the vector added product vector and input image vector. A computer program stored in a non-transitory computer readable medium causes a computer to perform the imaging method.

A method for imaging expected results of a medical cosmetic treatment includes converting an input image of an anatomical feature into an input image vector. A direction vector corresponding to the medical cosmetic treatment is determined. An amplitude of the direction vector is determined. The direction vector is multiplied by the determined amplitude to obtain a product vector. The product vector is vector added to the input image vector. An output image corresponding to the expected visual appearance of the human anatomical feature is generated from the vector added product vector and input image vector. A computer program stored in a non-transitory computer readable medium causes a computer to perform the imaging method.