Methods, systems, and apparatus, including computer programs encoded on computer storage media, for determining a subset of brain data of a patient. One of the methods includes obtaining data characterizing a brain of a patient; determining a first prompt for presentation to a user; obtaining a first user input characterizing a first response to the first prompt; determining, using the first response to the first prompt, a second prompt for presentation to the user; obtaining a second user input characterizing a second response to the second prompt, wherein at least one of the first prompt or the second prompt seek a response based on a clinical observation of the patient; and determining a subset of the obtained data using the first response to the first prompt and the second response to the second prompt.

Aspects of the present disclosure describe systems and methods for predicting an intra-aortic pressure of a patient receiving hemodynamic support from a transvalvular micro-axial heart pump. In some implementations, an intra-aortic pressure time series is derived from measurements of a pressure sensor of the transvalvular micro-axial heart pump and a motor speed time series is derived from a measured back electromotive force of a motor of the transvalvular micro-axial heart pump. Furthermore, in some implementations, machine learning algorithms, such as deep learning, are applied to the intra-aortic pressure and motor speed time series to accurately predict an intra-aortic pressure of the patient. In some implementations, the prediction is short-term (e.g., approximately 5 minutes in advance).

Aspects of the present disclosure describe systems and methods for predicting an intra-aortic pressure of a patient receiving hemodynamic support from a transvalvular micro-axial heart pump. In some implementations, an intra-aortic pressure time series is derived from measurements of a pressure sensor of the transvalvular micro-axial heart pump and a motor speed time series is derived from a measured back electromotive force of a motor of the transvalvular micro-axial heart pump. Furthermore, in some implementations, machine learning algorithms, such as deep learning, are applied to the intra-aortic pressure and motor speed time series to accurately predict an intra-aortic pressure of the patient. In some implementations, the prediction is short-term (e.g., approximately 5 minutes in advance).

Among other things, methods, systems and computer program products for providing visual indication of documentation and coding of medical procedures may include providing a choice of medical codes associated with a medical procedure. A user selection of one of the medical codes is detected. Based on the detection, a visual indication of the user selection is generated on one or more anatomical diagrams.

Among other things, methods, systems and computer program products for providing visual indication of documentation and coding of medical procedures may include providing a choice of medical codes associated with a medical procedure. A user selection of one of the medical codes is detected. Based on the detection, a visual indication of the user selection is generated on one or more anatomical diagrams.

Embodiments described herein provide various examples of a machine-learning-based visual-haptic system for constructing visual-haptic models for various interactions between surgical tools and tissues. In one aspect, a process for constructing a visual-haptic model is disclosed. This process can begin by receiving a set of training videos. The process then processes each training video in the set of training videos to extract one or more video segments that depict a target tool-tissue interaction from the training video, wherein the target tool-tissue interaction involves exerting a force by one or more surgical tools on a tissue. Next, for each video segment in the set of video segments, the process annotates each video image in the video segment with a set of force levels predefined for the target tool-tissue interaction. The process subsequently trains a machine-learning model using the annotated video images to obtain a trained machine-learning model for the target tool-tissue interaction.

A method of displaying an operational parameter of a surgical system is disclosed. The method includes receiving, by a cloud computing system of the surgical system, first usage data, from a first subset of surgical hubs of the surgical system; receiving, by the cloud computing system, second usage data, from a second subset of surgical hubs of the surgical system; analyzing, by the cloud computing system, the first and the second usage data to correlate the first and the second usage data with surgical outcome data; determining, by the cloud computing system, based on the correlation, a recommended medical resource usage configuration; and displaying, on respective displays on the first and the second subset of surgical hubs, indications of the recommended medical resource usage configuration.

The preset invention is an inventory-parts control system and supervisory arrangement suitable for tracking the actual use of and accounting for the ultimate disposition of at least some of the individual pieces constituting an implant (or a complete implant construct), which is then being surgically engrafted in-vivo. This inventory-parts control system and supervisory arrangement is accurate, reliable, and fully functional for inventory part control purposes during the surgery; and will account for the ultimate disposition of each required component item of that implant (or complete implant construct) individually and in a timely manner, as each part is individually surgically introduced and engrafted in-vivo at a preselected anatomic site upon or within the body of a living subject.

The preset invention is an inventory-parts control system and supervisory arrangement suitable for tracking the actual use of and accounting for the ultimate disposition of at least some of the individual pieces constituting an implant (or a complete implant construct), which is then being surgically engrafted in-vivo. This inventory-parts control system and supervisory arrangement is accurate, reliable, and fully functional for inventory part control purposes during the surgery; and will account for the ultimate disposition of each required component item of that implant (or complete implant construct) individually and in a timely manner, as each part is individually surgically introduced and engrafted in-vivo at a preselected anatomic site upon or within the body of a living subject.

A blood treatment machine includes a patient comfort feedback mechanism configured to be adjusted by a patient to indicate comfort levels of the patient. The machine is configured to adjust one or more treatment parameters based on the patient feedback.