Tissue Identification
TCP
Tissue Conductivity Profiling
Tissue Conductivity Profiling (TCP) measures bioimpedance across multiple frequencies at the working surface of the instrument. Fat, muscle, nerve, blood vessel, and tumor each conduct current differently across that frequency range, so the frequency response identifies tissue type and tissue condition. Multi-Vector Impedance Acquisition (MVIA) takes these measurements across a range of frequencies using electrodes activated at various angles, revealing data patterns that identify tissue types and conditions by their electrical “fingerprint.” From these, the system constructs a spatially resolved profile of the tissue contacting the sensing surface, updated continuously as the instrument advances.
Any instrument that needs to know what it is touching can use TCP. In biopsy, it helps confirm that the tip is in the target tissue before a sample is taken. In tissue targeting, it guides the instrument toward the intended tissue; in tissue avoidance, it flags nerves, vessels, and other structures to spare. When the tissue at the tip does not match what the preoperative images predict, it signals a possible image navigation error. For surgical robots, it adds live feedback from the instrument–tissue interface. Applications extend to virtually any instrument that touches tissue.
Precision Electrical Field Shaping
PIFS
Programmable Impedance Field Shaping
Programmable impedance elements can be used in conjunction with emerging precision field-shaping technologies, which have typically relied on commanded current or voltage alone to direct applied electrical fields. In addition to the actively driven electrodes, other electrodes can be assigned variable impedances that further shape and steer the electrical field.
Nerve mapping with unprecedented precision
High-Resolution Nerve Localization
Adaptive Closed-Loop Localization
An adaptive closed-loop system computes a probabilistic three-dimensional estimate of where a nerve lies and which way it runs.
The loop runs stimulation, recording, and evoked-potential analysis continuously. Each new set of evoked-potential data is analyzed as it arrives, and the stimulation and recording configurations adapt in real time to systematically home in on the location of the nerve pathway. This supports applications where nerve pathways must be located with great precision, such as surgical nerve mapping, and can improve the efficiency and effectiveness of peripheral nerve blocks in regional anesthesia.
Dynamic Neural Function Analysis
Evoked Potential Optimization
Strongest Signal, Lowest Stimulation
Evoked potential optimization searches across stimulation and recording configurations for the clearest, most consistent evoked responses.
Each response is analyzed as it arrives, and the system steers toward the strongest signal requiring the lowest effective stimulation intensity. Automated methods optimize neurophysiological data acquisition in ways that are impractical with traditional manual methods. The result is consistent, high-quality neurophysiological data that is less dependent on clinician skill, improving the diagnostic utility of the functional nerve assessments commonly used in intraoperative neuromonitoring and nerve conduction studies.
Global Neuromonitoring Platform
Multi-Node Integration and Neurological Outcome Prediction
The Global Neuromonitoring Platform gathers optimized neurophysiological data from every monitored pathway into a single functional evaluation of the nervous system.
Pattern analysis across modalities guides clinicians toward better signal acquisition, rules out technical problems, and separates anesthetic effects from true nerve compromise. In the example shown, a reduced left ulnar somatosensory evoked potential (SSEP) together with a reduced left abductor digiti minimi motor evoked potential (MEP), with every other pathway normal, points to positional compression of the left ulnar nerve at the elbow. The platform alerts the team and suggests repositioning the arm, and both responses return to baseline.
Each case is compared with normative data and prior surgeries with known outcomes. The data set grows with every surgery, and the neurological outcome prediction is designed to become more accurate over time.