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The SafeOp 2: Neural Informatix System is intended for use in monitoring neurological status by recording somatosensory evoked potentials (SSEP), electromyography (EMG), or assessing the neuromuscular junction (NMJ). Neuromonitoring procedures include intracranial, intratemporal, extratemporal, neck dissections, upper and lower extremities, spinal degenerative treatments, pedicle screw fixation, intervertebral fusion cages, rhizotomy, orthopedic surgery, open/percutaneous, lumbar, thoracic, and cervical surgical procedures.

The SafeOp™ 2: Neural Informatix System (SafeOp 2 System), formerly known as EPAD 2 (K182542), consists of the SafeOp head unit with power supply and IV pole mount, the Alpha Informatix Tablet with docking station and power supply and a data transfer USB cable. Associated disposable accessories consists of an electrode harness, surface and/or subdermal needle electrodes, and stimulating probe or clip contained in various kits. The SafeOp 2 System head unit contains a complete data acquisition system that has builtin stimulators, amplifiers, relays, A/D Converters, Digital Signal Processors, CPUs, and storage devices. The head unit interfaces with other equipment through communication ports and serves as the patient-contacting portion of the system where it is close to the surgical field. The head unit hardware contains an eight acquisition (input) channel and six-output channel Evoked Potential Stimulator that is used in the operating room to display nerve and muscle responses. The user can use these responses to diagnose insults to the peripheral or central nerves and to determine relative nerve location, proximity, and integrity data. The SafeOp 2 System application provides the primary graphical user interface and controls for the SafeOp 2 System. The application runs on a touchscreen tablet mobile device which connects to the head unit either via wired USB cable or wireless via Wi-Fi, enabling both user input (e.g., patient and procedure information, adjustment of stimulus and acquisition parameters) and display of output (e.g., display of acquired waveforms, data, messages and alerts to the clinician).

The provided text is a 510(k) summary for the SafeOp 2: Neural Informatix System. It primarily focuses on demonstrating substantial equivalence to a predicate device (EPAD™ 2 System, K182542) rather than presenting a detailed study proving the device meets specific acceptance criteria based on clinical performance.

Here's a breakdown of the requested information based on the provided text:

1. Table of acceptance criteria and the reported device performance

The document does not explicitly state quantitative "acceptance criteria" and "reported device performance" in the typical sense of a clinical study measuring diagnostic accuracy or treatment efficacy. Instead, it demonstrates "substantial equivalence" to a predicate device by comparing various specifications. The "acceptance criteria" for this submission would broadly be that the device's specifications and performance are comparable to the predicate, with any differences not introducing new safety or effectiveness concerns.

2. Sample size used for the test set and the data provenance (e.g. country of origin of the data, retrospective or prospective)

The document states: "Determination of substantial equivalence is not based on an assessment of clinical performance data." This indicates that there was no human test set or clinical data involved in proving substantial equivalence for this submission. The evaluation was based on nonclinical performance testing.

3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts (e.g. radiologist with 10 years of experience)

Not applicable, as no human test set or clinical data was used to establish substantial equivalence.

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

Not applicable, as no human test set or clinical data was used to establish substantial equivalence.

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. The device is for intraoperative neuromonitoring, not an AI-assisted diagnostic tool that would typically involve a multi-reader multi-case study for diagnostic improvement. The document explicitly states: "Determination of substantial equivalence is not based on an assessment of clinical performance data."

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

The document states that "Nonclinical performance testing demonstrates that the subject SafeOp 2 System meets the functional, system, and software requirements." This indicates that standalone performance testing was conducted for the device's functions (e.g., EMC, electrical safety, usability), but this refers to the system as a whole, not a specific algorithm's performance in isolation from a human user in a diagnostic or interpretive capacity. It does not provide details on the specific performance metrics (e.g., accuracy, sensitivity, specificity) for standalone algorithm performance in interpreting neurological signals.

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

For the nonclinical performance testing (EMC, Electrical Safety, Usability), the ground truth would be based on engineering standards and predetermined functional specifications. For example, for electrical safety, the ground truth is compliance with recognized electrical safety standards (e.g., 60601-1). For usability, the ground truth is that the device "presents no adverse effect within the intended environment."

8. The sample size for the training set

Not applicable. The submission is for a neuromonitoring system and does not describe an AI/machine learning model that would require a "training set" in the conventional sense. The "training" here would refer to the development and testing of the engineering system.

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/machine learning model.

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