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CNS Envision is intended for use by qualified personnel in the review, and analysis of patient data collected using external physiological monitors. These data are: raw and quantitative EEG, recorded video data, generic vital signs, electrocardiography, electromyography, intracranial pressures, transcranial Doppler measurements, and Glasgow Coma Score.

CNS Envision includes the calculation and display of a set of quantitative measures intended to monitor and analyze the EEG waveforms. These include, for example, frequency bands, asymmetry, and burst suppression. These quantitative EEG measures should always be interpreted in conjunction with review of the original EEG waveforms.

The aEEG functionality included in CNS Envision is intended to monitor the state of the brain.

CNS Envision is intended for use by a physician or other qualified medical personnel who will exercise professional judgement in using the information. It is intended for use on patients of all ages.

This device does not provide any diagnostic conclusion about the patient's condition to the user.

CNS Envision is a Microsoft Windows-based software application that facilitates the review, annotation and analysis of patient data and physiological measurements. Some of these data, such as ECG, are displayed in raw format whereas other types, such as EEG, are analyzed and quantified by the software. The specific type of input data that are reviewable by CNS Envision software are: Raw electroencephalography (EEG) Quantitative EEG trends; density spectral arrays (DSA) spectral edge frequency (SEF), alpha-delta ratio (ADR), and amplitude EEG (aEEG) Video Generic vital signs which are heart rate (HR), respiration rate (RR), pulse oximetry (SpO2), blood pressure, arterial blood pressure (ABP), mean arterial pressure (MAP), and body temperature Electrocardiography (ECG) Electromyography (EMG) Intracranial pressure (ICP) Transcranial Doppler (TCD) measurements (e.g. spectral envelope, peak velocity, and pulsatility index; TCD measurement is collected by the predicate K080217 device's interface module which interfaces with the Spencer TCD device cleared in K002533, which was a predicate to the predicate K080217 Glasgow Coma Score (GCS); this parameter is manually entered on the CNS Monitor (K080217) with 3 total GCS scores by the user; the CNS software automatically sums the 3 scores and stores the data to provide a trend graph

CNS Envision also has several features to enable ease-of-use. For example, users may select customized layouts that provide data displays that can be tailored to their monitoring needs according to data sources. The subject device also offers customizable EEG montages that present raw EEG data to medical personnel for interpretation.

Unlike the predicate device, Component Neuromonitoring System™, the subject device does not perform direct data acquisition. Instead, it offers the ability to review data remotely or adjust the review speed.

The provided text is a 510(k) summary for the CNS Envision device. It describes the device's intended use, technological characteristics, and comparison to predicate devices, but it does not contain information about specific acceptance criteria, device performance metrics (e.g., sensitivity, specificity, accuracy), sample sizes for test or training sets, ground truth establishment details, or any multi-reader multi-case (MRMC) study results.

The document states that "Software verification and validation testing was conducted and documentation provided as recommended by the Guidance for the Content of Software Contained in Medical Devices, issued May 2005. Traceability has been documented between all system specifications to validation test protocols. Verification and validation testing includes module-level testing, integration-level testing, and system-level testing. In addition, tests according to “IEC 62366-1:2015, Medical Devices Part 1—Application of usability engineering to medical devices” were performed."

This indicates that some testing was done to ensure the software functions as intended and meets usability standards, but the specific performance results in terms of clinical accuracy or equivalent metrics are not present in this summary. The summary focuses on establishing substantial equivalence based on intended use and technological characteristics rather than a detailed performance study like those typically expected for AI/ML-based diagnostic devices.

Therefore, I cannot provide a table of acceptance criteria and reported device performance, or details about the sample sizes, expert ground truth, adjudication methods, MRMC studies, or standalone performance for this specific device based on the provided text. The device "CNS Envision" is described as software that analyzes and quantifies EEG, but "does not provide any diagnostic conclusion about the patient's condition to the user" and "does not contain automated detection algorithms," suggesting it's a tool for experts rather than an automated diagnostic AI.

To illustrate what such an answer would look like if the information were available, here's a template:

Hypothetical Example (based on standard AI/ML medical device studies, NOT from the provided text):

Given the provided document does not contain the requested information regarding specific acceptance criteria, performance metrics, training/test set details, or human reader studies, I cannot fill out the detailed table and answer the questions directly from the text.

However, if this were an AI-powered diagnostic device, the requested information would typically be presented as follows:

1. Table of Acceptance Criteria and Reported Device Performance (Hypothetical Example - Data NOT from provided text)

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Varia-NCI is a stand-alone software accessory to a Transcranial Doppler Ultrasound device (TCD) that retrieves, analyzes, and displays Cerebral Blood Flow velocity (CBFv) data from a Transcranial Doppler Ultrasound device. Varia-NCI uses CBFv data to measure the variability of a patient's cerebral blood flow velocity.

Varia-NCI is to be used by clinicians managing head trauma in the ICU, Surgical Unit, Emergency Department, and Clinical and Sports Medicine Settings.

Varia-NCI is a stand-alone software accessory to a Transcranial Doppler Ultrasound device (TCD) that retrieves, analyzes, stores, and displays Cerebral Blood Flow velocity (CBFv) data from a Transcranial Doppler Ultrasound device. Varia-NCI uses CBFv data to measure the variability of a patient's cerebral blood flow velocity. Varia-NCI accesses data from Compumedics Germany QL 3.0 software. QL 3.0 is a trade mark of PC-based software supplied by Compumedics Germany, GmbH and included with their digital Transcranial Doppler (TCD) Ultrasound device.

The provided text describes a 510(k) premarket notification for a device named Varia-NCI. However, it does not include a detailed acceptance criteria table, nor a study proving the device meets specific performance criteria in the way typically required for AI/ML-based diagnostic devices (e.g., sensitivity, specificity, AUC).

Instead, the submission focuses on demonstrating substantial equivalence to a predicate device (Compumedics Germany – Doppler-Box X) largely through non-clinical software testing and the absence of clinical testing requirements given its role as a software accessory and the established safety and effectiveness of the predicate.

Based on the provided information, here's a breakdown of what can be extracted and what is missing concerning your request:

1. Table of Acceptance Criteria and Reported Device Performance

The document provides a list of non-clinical software tests performed and their outcomes, indicating that the device "passed" each test. It does not provide quantitative performance metrics (e.g., accuracy, sensitivity, specificity) against specific numerical acceptance criteria.

2. Sample size used for the test set and the data provenance

The document details non-clinical software testing. It does not specify a "test set" in the context of clinical data or patient samples. The testing appears to have been performed using simulated or representative data relevant to software functions (e.g., "CBFv exp files were entered into the database"). Therefore, information regarding data provenance (country of origin, retrospective/prospective) is not applicable as described for a clinical test set.

3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts

This information is not provided. The testing described is software functionality testing, not a clinical study requiring expert-established ground truth on patient data.

4. Adjudication method (e.g., 2+1, 3+1, none) for the test set

Not applicable, as no clinical test set requiring expert adjudication is described.

5. If a multi-reader multi-case (MRMC) comparative effectiveness study was done, If so, what was the effect size of how much human readers improve with AI vs without AI assistance

There is no mention of an MRMC study or any study involving human readers and AI assistance. The device is a software accessory that processes and displays data, not an AI-assisted diagnostic tool for human readers.

6. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done

The software testing described is a form of standalone performance evaluation for the algorithm's functions. The "Test Passed" outcomes for "Calculation Testing and Display results" and "System Verification/Validation Performance Testing" demonstrate the algorithm's ability to accurately calculate and display CBFv variability. However, these are functional tests, not a clinical performance study using patient outcomes.

7. The type of ground truth used (expert consensus, pathology, outcomes data, etc)

For the software testing, the "ground truth" implicitly refers to the expected behavior and correct outputs of the software's functions, as defined by its specifications. For instance, in "Calculation Testing," the ground truth would be the accurately pre-computed or theoretically expected variability values against which the software's calculations were validated. It is not expert consensus, pathology, or outcomes data.

8. The sample size for the training set

Not applicable. The document does not describe an AI/ML model that requires a training set in the conventional sense. Varia-NCI is described as software that "retrieves, analyzes, and displays Cerebral Blood Flow velocity (CBFv) data" and "uses CBFv data to measure the variability of a patient's cerebral blood flow velocity." This implies deterministic algorithms rather than a machine learning model that would be "trained."

9. How the ground truth for the training set was established

Not applicable, as there is no mention of a training set for an AI/ML model.

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The Highland Instruments CES and TUS Instrument Holder is intended for use in assisting holding and securing commercially available CES electrodes and diagnostic Transcranial Ultrasound System Probes in the desired position on the patient's head.

The Highland Instrument CES and TUS Instrument Holder is intended to position and hold in position the ultrasound probe of a Transcranial Ultrasound device while at the same time assisting in maintaining the position of electrodes used with a Cranial Electrotherapy Stimulator (CES) device and keeping the electrode lead wires free from entanglement and otherwise free from being disturbed by the patient. The Highland Instrument Holder allows for a physician to utilize a commercially available CES device while simultaneously observing/measuring cerebral regional blood flow in discreet areas of the brain. It further allows the physician to utilize a commercially available CES device while imaging the brain with transcranial ultrasound imaging probes that do not contain the Doppler option.

The provided 510(k) summary for the Highland Instruments CES and TUS Instrument Holder does not contain information about acceptance criteria or a study proving the device meets specific performance metrics in the way that would typically be presented for a diagnostic or AI-driven device.

This document describes a medical device that is an "instrument holder," which is a physical device used to position and hold other medical instruments (ultrasound probes and CES electrodes). Its substantial equivalence is based on its functional design and its ability to hold instruments, rather than on performance metrics related to diagnostic accuracy, sensitivity, or specificity.

Therefore, many of the requested elements for describing "acceptance criteria and the study that proves the device meets the acceptance criteria" are not applicable or not present in this type of 510(k) submission for a physical positioning device.

Here's a breakdown of the available information based on your request:

1. Table of Acceptance Criteria and Reported Device Performance

Explanation: For this type of device, "acceptance criteria" are implied by its intended function and safety. The reported performance refers to "functional testing" and a "biocompatibility assessment," but detailed quantitative results or specific pass/fail criteria are not included in this summary.

Regarding the specific questions about studies proving the device meets acceptance criteria (which are more relevant to diagnostic/AI devices):

• 2. Sample size used for the test set and the data provenance: Not applicable. The "testing" mentioned is laboratory bench testing for mechanical function and biocompatibility, not a clinical study with a test set of patient data.
• 3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts: Not applicable. Ground truth for diagnostic performance is not relevant for an instrument holder.
• 4. Adjudication method for the test set: Not applicable.
• 5. If a multi reader multi case (MRMC) comparative effectiveness study was done, If so, what was the effect size of how much human readers improve with AI vs without AI assistance: Not applicable. This is not an AI device or a diagnostic device that requires reader performance evaluation.
• 6. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done: Not applicable. This is not an algorithm.
• 7. The type of ground truth used (expert consensus, pathology, outcomes data, etc): Not applicable. The "ground truth" for this device's performance would be its physical stability and ability to hold instruments as designed, verified through engineering tests.
• 8. The sample size for the training set: Not applicable. This is not an AI device requiring a training set.
• 9. How the ground truth for the training set was established: Not applicable.

Summary of the Study (Functional Testing and Biocompatibility Assessment):

The submission states that "Descriptive information, laboratory bench testing and a biocompatibility assessment were provided to demonstrate the device meets its design specifications, performs as intended, and is safe for its intended use."

• Type of Study: Laboratory bench testing and biocompatibility assessment.
• Objective: To demonstrate the device's ability to position and hold transducers/lead wires and its safety through material assessment.
• Methodology (briefly described): Testing was performed to demonstrate the ability of the device to position and hold a variety of transducers and lead wires. A biocompatibility assessment affirmed that the device introduces no new materials from consumer products.
• Results (general statement): The testing demonstrated the device's ability to perform its intended function and its safety. Specific quantitative results are not provided in this summary document.
• Conclusion: The device was deemed substantially equivalent to predicate devices based on its design, intended use, principles of operation, and the results of the functional and biocompatibility testing.

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The EMS9UA Transcranial Doppler Ultrasound System is intended for use as a diagnostic ultrasound fluid flow analysis system:

1. For the measurement of cerebral artery blood velocities to determine the presence of hemodynamically significant deviations from normal values
2. To assess arterial cerebral blood flow for the occurrence of micro embolic signals. Vessels intended for observation include, but are not limited to the middle, anterior and posterior cerebral arteries, via the temporal windows, the vertebral mid basilar arteries via the foramen magnum and the ophthalmic artery and intracranial internal carotid artery via the eye.

The Roboprobe Headband facilitates monitoring use by its ability to track the Doppler signal. The EMS9UA Transcranial Doppler is intended for use during:

a) Diagnostic exams
b) Surgical interventions

The device is not intended to replace other means of evaluating vital patient physiological processes, is not intended to be used in fetal applications, and is not intended to be used inside the sterile field.

Shenzhen Delicate believes that the Model EMS-9UA is substantially equivalent to its EMS OU Transcranial Doppler which was cleared on May 5, 2006 510kH K060112. The EMS-9UA has the same device description except that the head frame used for longer term monitoring has the ability to track the Doppler signal and therefore not lose the signal with patient movement and time. The tracking is accomplished by adding to the EMS9U an additional circuit which detects the ultrasound Doppler return and positions the face of the probe in the headband to maximize the detected ultrasound return. The headband electronics does not change or interfere with the transmitted ultrasound. Except for the servo motor controller added to the circuitry of the EMS9U range and the software added to control it, and the modifications to the INNSO Tange and the servo motor controlled probe, the EMS9U range and the EMS- 9UA are identical internally and functionally. The probes are identical to those cleared in K060112.

Here's a breakdown of the acceptance criteria and study information for the EMS9UA Transcranial Doppler with Robotic Probe Headband, based on the provided document:

Acceptance Criteria and Device Performance

The document does not explicitly state quantitative performance acceptance criteria (e.g., specific accuracy thresholds) for the EMS9UA device. Instead, the "acceptance criteria" are implied by compliance with safety and industry standards, and the demonstration of safety and effectiveness through performance testing and a clinical trial, showing substantial equivalence to the predicate device.

The reported device performance emphasizes its capabilities as a diagnostic ultrasound fluid flow analysis system for measuring cerebral artery blood velocities and detecting micro embolic signals, with the added functionality of the robotic headband to track the Doppler signal.

Table of Acceptance Criteria and Reported Device Performance:

Study Information

The document describes a clinical trial, though details are limited.

1. Sample Size Used for the Test Set and Data Provenance:

   • Sample Size: 100 patients.
   • Data Provenance: Not explicitly stated (e.g., country of origin). The study is referred to as a "clinical trial," which typically implies prospective data collection, but this is not explicitly confirmed.

2. Number of Experts Used to Establish the Ground Truth for the Test Set and Qualifications of Those Experts:

   • This information is not provided in the document. The study compared the EMS9UA with a predicate device, but it doesn't describe who evaluated the outputs or established a ground truth for the comparison.

3. Adjudication Method for the Test Set:

   • This information is not provided in the document.

4. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study Was Done:

   • Yes, a comparative study was done. A "clinical trial involving 100 patients was conducted comparing the EMS9UA Transcranial Doppler with Robotic Headband with the Spencer Technologies Marc 600 predicate headband."
   • Effect Size: The document states that the device "was found to be safe and effective" in comparison to the predicate, implying non-inferiority or similar performance. However, no specific quantitative effect size or details on how human readers improved with AI vs. without AI assistance are provided. The robotic headband's function is to track the Doppler signal and not lose the signal with patient movement and time, which is a practical improvement in monitoring, rather than an AI-driven interpretive enhancement for human readers. This device's primary improvement is in signal stability and monitoring duration, not necessarily in diagnostic interpretation per se.

5. If a Standalone (i.e., algorithm only without human-in-the-loop performance) Was Done:

   • The document implies that the device is a diagnostic ultrasound system intended for use by "trained medical personnel." It is not described as an AI algorithm providing standalone diagnoses. The robotic probe head is an automated feature for signal tracking, which enhances the device's performance, but it's not suggested that it operates entirely without human oversight or interpretation of the ultrasound data. Therefore, a standalone algorithm-only performance study as typically understood for AI diagnostics is not applicable/not described.

6. The Type of Ground Truth Used:

   • This is not explicitly stated. Given that it's a comparative study against a predicate device, the "ground truth" for the comparison would likely be the diagnostic output or clinical utility of the predicate device. However, details on how this was established or if an independent clinical ground truth (e.g., pathology, follow-up outcomes) was used are not provided.

7. The Sample Size for the Training Set:

   • This is not applicable/not provided. The document describes a medical device (Transcranial Doppler with a robotic headband for signal tracking), not a typical AI model that undergoes a separate training phase with a distinct training dataset. The "training" for such a device would involve engineering design, calibration, and internal verification.

8. How the Ground Truth for the Training Set Was Established:

   • This is not applicable/not provided for the reasons stated above.

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