A system includes a processor circuit configured to receive a first set of data. The first set of data includes two pressure measurements and a flow measurement from the vasculature of a patient obtained while the sympathetic nervous system of the patient is not under stimulation. The processor circuit calculates a blood flow resistance value based on the first set of data. The processor circuit then receives a second set of data. The second set of data also includes two pressure measurements and a flow measurement from the vasculature of the patient obtained while the sympathetic nervous system of the patient is stimulated. The processor circuit calculates another blood resistance value based on the second set of data. The processor circuit then compares the two blood flow resistance values to determine whether a denervation procedure would be effective to mitigate the nerve system's response to stimulation. The processor circuit outputs to a screen display metrics obtained from the measurement procedure.

A system includes a processor circuit in communication with an anatomical measurement device. The anatomical measurement device receives a metric associated with a sympathetic response of a patient. The sympathetic nervous system of the patient is then stimulated. The anatomical measurement device then receives another metric associated with a sympathetic response of the patient while the sympathetic nervous system is stimulated. The processor circuit then provides an output based on the comparison.

Embodiments for crossing an occlusion by controlling a guide with the aid of optical coherence tomography (OCT) data are described. Embodiments include transmitting one or more beams of radiation via one or more waveguides on a flexible substrate within a guide wire. One or more beams of scattered or reflected radiation may be received from a sample via one or more waveguides. Depth-resolved optical data of the sample may be generated based on the received beams of scattered or reflected radiation. The depth-resolved data may be used for determining at least one of a distance between the guide wire and a wall of the artery and a distance between the guide wire and an occlusion within the artery. A position of the guide wire within the artery may then be controlled based on the determined distance or distances.

Embodiments described relate to techniques for identifying and characterizing biological structures using machine learning techniques. These techniques may be employed to enable a device to identify the particular type of tissue and/or cells (e.g., platelets, smooth muscle cells, or endothelial cells) in, for example, a biological structure, which may be a tissue or a lesion of a duct (e.g., vasculature) in an animal (e.g., a human or non-human animal), among other structures. The machine learning techniques may use raw impedance spectroscopy measurement data in addition to values derived from that raw data. In addition, the machine learning techniques may be used to select frequencies at which to measure impedance and select features to extract from the measured impedance at the selected frequencies to arrive at a small set of frequencies that allow for reliable differentiation.

Systems for determining fractional flow reserve are disclosed. An example system may include a pressure sensing guidewire for measuring a first pressure, a second pressure sensing medical device for measuring a second pressure, and a processor coupled to the pressure sensing guidewire and coupled to the second pressure sensing medical device. The processor may be designed to generate a plot of the magnitude of the second pressure over time, identify one or more time intervals of the plot that have a slope less than zero, determine a mean of the second pressure, and calculate the ratio of the first pressure to the second pressure when (a) the second pressure is less than or equal to the mean of the second pressure and (b) during the one or more time intervals when the slope of the plot is less than zero.

A state assessment system, a diagnosis and treatment system and a method for operating the diagnosis and treatment system are disclosed. An oscillator model converts a physiological signal of a subject into a defined feature image. A classification model analyzes state information of the subject based on the feature image. An analysis model outputs a treatment suggestion for the subject based on the state information of the subject. An AR projection device projects acupoint positions of a human body onto the subject, for the subject to be treated based on the treatment suggestion.

An apparatus for estimating bio-information, includes a sensor including a cover having a transmitting area provided at a center of the cover, and non-transmitting areas provided at both edges of the cover, a light source configured to emit light onto an object that is in contact with the cover, and a detector configured to detect a first optical signal of the emitted light that is scattered or reflected from the object after passing through the transmitting area, and a second optical signal of the emitted light that is reflected from the non-transmitting areas. The apparatus further includes a processor configured to estimate bio-information, based on the detected first optical signal and the detected second optical signal.

A method measures a perfusion wave upstroke associated with leg perfusion dynamics, the perfusion wave upstroke including two phases, an initial slow phase and a fast-rising phase, and using prolongation of the slow phase to detect a presence of arterial stenosis and to assess stenosis severity.

A computing system (118) includes a computer readable storage medium (122) with computer executable instructions (124), including a biophysical simulator (126) and an electrocardiogram signal analyzer (128). The computing system further includes a processor (120) configured to execute the electrocardiogram signal analyzer determine myocardial infarction characteristics from an input electrocardiogram and to execute the biophysical simulator to simulate a fractional flow reserve or an instant wave-free ratio index from input cardiac image data and the determined myocardial infarction characteristics.

A first optical measurement of tissue with a first optical device is initiated. The first optical measurement includes a first shallow optical reading and a first deeper optical reading. A second optical measurement of the tissue with a second optical device spaced is initiated. The second optical device is spaced apart from the first optical device. The second optical measurement includes a second shallow optical reading and a second deeper optical reading. A first difference value between the first shallow optical reading and the first deeper optical reading is determined. A second difference value between the second shallow optical reading and the second deeper optical reading is determined. A large vessel occlusion (LVO) alert is generated when a ratio of the first difference value to the second difference value is larger than a threshold value.