Method and system for monitoring an internal electrical impedance of a biological object including Internal Thoracic Impedance (ITI) comprising placing two arrays of electrodes on opposite sides of the biological object, wherein each of said two arrays comprise three equally spaced electrodes; imposing an alternating electrical current between pairs of the electrodes and obtaining voltage signals representative of a voltage drop thereon, calculating two values of internal electrical impedance of the biological object corresponding to the uttermost electrodes of said two arrays of electrodes placed on the opposite sides of the biological object.

Examples herein may include a computer-implemented method for providing outcome tracking of patients, which may include generating an event trigger for the patient, wherein during a duration of the patient's recovery, the event trigger may correspond to values of a patient biomarker over or under threshold values while the patient is performing a post-surgery activity related to the patient's recovery. The computer-implemented method may include receiving actual patient biomarker data from a patient sensor system for the patient while the patient is performing a post-surgery activity. If the actual patient biomarker data includes values over or under the threshold value while the patient is performing a post-surgery activity, the method may include triggering the event trigger. The method may include generating a notification alert corresponding to the event trigger.

Tissue irregularity complication(s) may be predicted based on biomarker measurements obtained before a surgery and/or during the surgery via one or more sensing systems. For example, a computing system may monitor the patient biomarker(s) including tissue perfusion pressure, lactate, oxygen saturation, VO2Max, respiration rate, autonomic tone, sweat rate, heart rate variability, skin conductance, GI motility, edema and/or hydration state. Based on the prediction, the computing system may generate a control signal configured to alter a matter in which a surgical cutting and stapling device and/or a surgical energy operate, to adjust a surgical procedure plan, to adjust a surgical instrument selection, indicate a probability of the tissue irregularity complication, and/or to indicate a suggested adjustment to surgical procedure plan, surgical approach, and/or surgical instrument selection.

A computing system may predict a complication based on measurements of related biomarker(s) obtained via one or more sensing systems, and generate an adjustment of a surgical parameter associated with a surgery. The biomarker measurements may include pre-surgical and/or in-surgical measurements. The hemostasis-related biomarker(s) measured pre-surgery may include blood pressure, blood pH, edema, heart rate, blood perfusion rate, coagulation status and/or the like. Based on the prediction, the computing system may generate a control signal configured to alter a matter in which a surgical cutting and stapling device and/or a surgical energy operate, to adjust a surgical procedure plan, to adjust to a surgical instrument selection, indicate a probability of the hemostasis complication, and/or to indicate a suggested adjustment to surgical procedure plan, surgical approach, and/or surgical instrument selection.

A surgical computing system may receive usage data associated with movement of a surgical instrument and user inputs to the surgical instrument. The surgical computing system may receive motion and biomarker sensor data from sensing systems applied to the operator of the surgical instrument. The surgical computing system may determine, based on at least one of the usage data and/or the sensor data, an evaluation of the actions of the operator of the surgical instrument. The surgical computing system may determine, based on the evaluation, to provide feedback. The feedback may comprise instructions for the surgical instrument to provide haptic feedback and/or to modify its configuration. The feedback may comprise instructions for a display unit to present notifications instructing the healthcare professional. The surgical computing system may communicate instructions for providing the feedback to the surgical instrument and/or the display unit.

A computing system for measuring and monitoring patient biomarkers for detecting or predicting a post-surgical colorectal complication may be provided. A post-surgical colorectal complication may be predicted or detected by comparing measured/processed patient biomarker data with a corresponding determined threshold value. The comparison of the measured/processed patient biomarker data and the corresponding threshold may be performed in association with a context. The context may be based on at least one of a colorectal surgery recovery timeline, at least one situational attribute, or at least one environmental attribute. A notification message associated with a predicted or detected post-surgical colorectal complication may be sent (e.g., sent in real time) to a patient device or a healthcare provider's device. The notification message may be supplemented by a severity level message.

A method for use in analysing impedance measurements performed on a subject, the method including, in a processing system, determining at least one impedance value, representing the impedance of at least a segment of the subject, determining and indicator indicative of a subject parameter using the at least one impedance value and a reference, and displaying a representation of the indicator

A wearable monitoring device can include an electronics module containing a printed circuit board (PCB) to which one or more sensors are coupled. The one or more sensors can include one or more contacting sensors and/or one or more non-contacting sensors. In some cases, the wearable monitoring device can include an onboard power supply (e.g., a battery) and a wireless communication antenna. The PCB can be constructed to specifically include the one or more sensors on a first side facing the skin of the user when the wearable monitoring device is being worn, allowing one or more processors, memory, and other components to be included on the opposite side facing away from the user. Certain components, such as the power supply and wireless communication antenna, can be spaced apart from the PCB and located opposite the PCB from the one or more sensors.

A method of identifying fluid retention in a body part of the patient by directly or indirectly measuring a first parameter relating to a size of a body part of the patient to obtain an actual measurement of the body part, obtaining an estimated measurement of said first parameter relating to the size of the body part of the patient by measuring alternative predefined parameters of the patient, wherein said estimated measurement is calculated based on a mathematical relationship between said alternative parameters and the size of the body part; and correlating the actual and estimated measurements of the body part of the patient to assess any fluid retention in the body part.

An eye disease diagnosis method using artificial intelligence may include: collecting, from a database, a first eyeground image of a myopic patient who is not diagnosed with an eye disease and a second eyeground image of a myopic patient who has been diagnosed with the eye disease; learning eyeball change information by degree of myopia based on the first eyeground image, using deep learning; comparing and analyzing the first eyeground image and the second eyeground image based on the eyeball change information by the degree of myopia, and learning eyeball change information by the eye disease using deep learning; and estimating determination criteria of an eyeground image for diagnosis of the eye disease, based on a difference between the eyeball change information by the degree of myopia and the eyeball change information by the eye disease.