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The SafeOp 3: Neural Informatix System is intended for use in monitoring neurological status by recording transcranial motor evoked potentials (MEP), somatosensory evoked potentials (SSEP), electromyography (EMG), or assessing the neuromuscular junction (NMJ). Neuromonitoring procedures include intracranial, extracranial, 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.

SafeOp 3 Accessories: The SafeOp Accessories are utilized in spine surgical procedures to assist in location of the nerves during or after preparation and placement of implants (intervertebral fusion cages and pedicle screw fixation devices) in open and percutaneous minimally invasive approaches.

The SafeOp™ 3: Neural Informatix System (SafeOp 3 System), consists of the SafeOp patient interface 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, MEP Activator, Cranial Hub, Delta Dilators and Stimulating Probe and Delta Clip contained in various kits.

The subject device is intended for use by trained healthcare professionals, clinical neurophysiologists/technologists and appropriately trained non-clinical personnel. The subject device is intended for use in operating room environments of hospitals and surgical centers. System setup may be performed by both clinical and trained non-clinical personnel.

The provided FDA 510(k) clearance letter and summary for the SafeOp 3: Neural Informatix System (K252842) primarily describe the device's substantial equivalence to a predicate device (K234092) and outline a Predetermined Change Control Plan (PCCP) for future modifications. However, it does not include a detailed study proving the device meets specific acceptance criteria in the context of clinical performance or AI algorithm validation for the initial clearance.

The "Performance Data" section explicitly states: "Determination of substantial equivalence is not based on an assessment of clinical performance data." and refers to nonclinical performance testing (functional, system, software requirements) and usability testing.

Therefore, the following information is extracted and synthesized based on the available document, but a comprehensive study proving acceptance criteria for all aspects of neural informatic performance is not present. The document focuses on the future PCCP for algorithmic changes, which will have performance criteria.

Acceptance Criteria and Reported Device Performance

The core of the documentation for this 510(k) clearance is about demonstrating substantial equivalence to an existing predicate device (SafeOp 3™: Neural Informatix System, K234092). Therefore, the "acceptance criteria" for the cleared device itself are primarily its ability to match the predicate's performance and specifications, which is shown through a direct comparison.

The document does not report specific quantitative performance metrics for the SafeOp 3: Neural Informatix System for its initial clearance, as it relies on substantial equivalence.

However, the Predetermined Change Control Plan (PCCP) outlines future modifications to the SSEP Baseline Classification Algorithm and defines acceptance criteria for these future algorithmic changes. These are the only explicitly stated performance acceptance criteria in the document.

Table 1: Acceptance Criteria and Reported Device Performance (from PCCP for future algorithmic changes)

Study Details (Based on the document, for the initial clearance)

Because the 510(k) is based on substantial equivalence and explicitly states "Determination of substantial equivalence is not based on an assessment of clinical performance data," there is no detailed "study" proving the device meets acceptance criteria in the way a de novo or PMA submission might. The "study" mentioned below refers to the verification and validation activities conducted to demonstrate substantial equivalence and safety/effectiveness for the initial clearance, as described in the "Performance Data" section.

1. Sample size used for the test set and the data provenance:

   • Not explicitly stated for clinical performance data for the initial clearance. The document mentions "nonclinical performance testing" for functional, system, and software requirements, and "usability testing." These typically involve controlled test cases and user interactions rather than a large clinical test set.
   • For the future PCCP modifications of the SSEP algorithm, it mentions "an independent, firewalled dataset" and "additional labeled data" for re-training and testing, but no sample size or provenance is specified within this document.

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

   • Not explicitly stated for the initial clearance. Since the clearance is based on substantial equivalence and does not assess clinical performance data, detailed expert ground truth establishment for a clinical test set is not described.

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

   • Not explicitly stated for the initial clearance. Not applicable given the reliance on substantial equivalence and lack of clinical performance data assessment.

4. 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:

   • No. The document does not describe an MRMC study. The device is a neural informatic system, and while it involves signal processing, it's not positioned as an AI-assisted diagnostic tool that human readers would interpret with assistance in the traditional sense of an MRMC study. The SSEP algorithm mentioned in the PCCP appears to be a standalone classification, not a human-in-the-loop assistance tool subject to such a study.

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

   • For the initial clearance, the document refers to "nonclinical performance testing" demonstrating the system meets functional, system, and software requirements. This implies standalone testing of device functions.
   • For the future SSEP Baseline Classification Algorithm (PCCP Modification #1 & #4), the acceptance criteria (PPV, Accuracy, F1-score) clearly indicate standalone algorithm performance is intended to be evaluated. However, no such standalone performance data is provided for the initially cleared device.

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

   • Not explicitly stated for the initial clearance. For the "nonclinical performance testing" and "usability testing," ground truth would typically be established based on engineering specifications and validated operational procedures.
   • For the future SSEP Baseline Classification Algorithm (PCCP Modification #1 & #4), it refers to "labeled data," which implies a ground truth established by experts or other definitive methods, but the specific type is not detailed in this document.

7. The sample size for the training set:

   • Not explicitly stated for the initial clearance. No AI algorithms are reported as being evaluated for the initial clearance.
   • For the future SSEP Baseline Classification Algorithm (PCCP Modification #1 & #4), it mentions the algorithm "will be re-trained using site-specific data" and "periodically re-trained as additional labeled data become available," but no sample size is specified within this document.

8. How the ground truth for the training set was established:

   • Not explicitly stated for the initial clearance.
   • For the future SSEP Baseline Classification Algorithm (PCCP Modification #1 & #4), it refers to "labeled data," but the method of labeling (establishing ground truth) is not detailed in this document.

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