The present disclosure relates to a data acquisition device and a configuration method. The device includes a channel, wherein the channel includes a data control panel and a plurality of detection components. At least one of the plurality of detection components is directly connected to the data control panel. The data control panel may be configured to identify the channel and send a configuration command to the plurality of detection components. The plurality of detection components may determine channel location numbers of the plurality of detection components based on the configuration command and send the channel location numbers to the data control panel. The data control panel may determine identification numbers for the plurality of detection components based on the channel location numbers and allocate the identification numbers to the plurality of detection components.

The present disclosure relates to a data acquisition device and a configuration method. The device includes a channel, wherein the channel includes a data control panel and a plurality of detection components. At least one of the plurality of detection components is directly connected to the data control panel. The data control panel may be configured to identify the channel and send a configuration command to the plurality of detection components. The plurality of detection components may determine channel location numbers of the plurality of detection components based on the configuration command and send the channel location numbers to the data control panel. The data control panel may determine identification numbers for the plurality of detection components based on the channel location numbers and allocate the identification numbers to the plurality of detection components.

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.

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.

An apparatus for real time monitoring of a patient is provided and includes a memory element for storing data, a processor that executes instructions associated with the data, an interface that receives sensor data from a sensor that takes measurements from the patient and sends the sensor data according to the sensor's measurement latency, a latency calculator that frequently calculates a latency threshold that varies according to at least a health status of the patient, a timer that continuously monitors the sensor's measurement latency, a comparator that frequently compares the sensor's measurement latency with the calculated latency threshold, and a feedback module that automatically changes the sensor's measurement latency to match with the calculated latency threshold.

An apparatus for real time monitoring of a patient is provided and includes a memory element for storing data, a processor that executes instructions associated with the data, an interface that receives sensor data from a sensor that takes measurements from the patient and sends the sensor data according to the sensor's measurement latency, a latency calculator that frequently calculates a latency threshold that varies according to at least a health status of the patient, a timer that continuously monitors the sensor's measurement latency, a comparator that frequently compares the sensor's measurement latency with the calculated latency threshold, and a feedback module that automatically changes the sensor's measurement latency to match with the calculated latency threshold.

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.

The invention provides for a magnetic resonance imaging system (100) for acquiring magnetic resonance data (142) from a subject (118) within a measurement zone (108). The magnetic resonance imaging system (100) comprises: a processor (130) for controlling the magnetic resonance imaging system (100) and a memory (136) storing machine executable instructions (150, 152, 154), pulse sequence commands (140) and a dictionary (144). The pulse sequence commands (140) are configured for controlling the magnetic resonance imaging system (100) to acquire the magnetic resonance data (142) of multiple steady state free precession (SSFP) states per repetition time. The pulse sequence commands (140) are further configured for controlling the magnetic resonance imaging system (100) to acquire the magnetic resonance data (142) of the multiple steady state free precession (SSFP) states according to a magnetic resonance fingerprinting protocol. The dictionary (144) comprises a plurality of tissue parameter sets. Each tissue parameter set is assigned with signal evolution data pre-calculated for multiple SSFP states.

There is provided a system for monitoring transpulmonary pressure of a mechanically ventilated individual, comprising: a feeding tube, at least one esophageal body, a pressure sensor, and a memory having stored thereon code for: computing an estimate of esophageal wall pressure according to pressure in the esophageal body when inflated and contacting the inner wall of the esophagus, computing the transpulmonary pressure of the mechanically ventilated target individual according to the esophageal wall pressure, periodically inflating and deflating the esophageal body for periodic monitoring of the transpulmonary pressure of the mechanically ventilated target patient while the feeding tube is in use, and computing instructions for adjustment of parameter(s) of a mechanical ventilator that automatically ventilates the target individual according to the computed transpulmonary pressure, wherein the instructions for adjustment of parameter(s) of the mechanical ventilator are computed while the feeding tube is in place without removal of the feeding tube.