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The StimRouter Neuromodulation System is indicated for pain management in adults who have severe intractable chronic pain of peripheral nerve origin, as an adjunct to other modes of therapy (e.g., medications). The StimRouter is not intended to treat pain in the craniofacial region.

The StimRouter Neuromodulation System consists of two main parts - the implantable lead, and the external (to the body) accessories for the StimRouter include a clinician programmer with software (CPS), a disposable hydrogel electrode patch, an external pulse transmitter, an external pulse transmitter stimulation tester and a device used by the patient to wirelessly control the external pulse transmitter. The StimRouter Neuromodulation System is provided with three labeling documents: the Clinician's Guide, the Procedure Manual and the User's Guide. The complete StimRouter System consists of three kits: a Lead and Lead Introducer Kit, a Clinician Kit and User Kit. The Lead Kit contains the StimRouter implantable multielectrode lead with integrated receiver, used for peripheral nerve stimulation. The Lead receives an electrical signal transmitted transcutaneously by the external pulse transmitter which is mounted on an electrode patch on the skin and delivers that electrical signal down the lead's length to a target peripheral nerve. The Lead is supplied in Lead Loader that is used during intraoperative testing of the lead and to verify proper placement during implantation. The Lead and Lead Introducer Kit consists of two stimulation probes, two stimulation cables, and introducer set, a lead adapter, a Tunneling Needle, and a Tunneling Needle Stylet. The included tools and components allow for insertion of the StimRouter Lead and confirmation of optimal location of the stimulation electrode contacts of the StimRouter Lead. The Clinician Kit is used for the programming of the external pulse transmitter. The components of the Clinician Kit are a tablet PC with programming software that is capable of connecting to and configuring the external pulse transmitter. The User Kit contains the patient-use components of the StimRouter System. The components are the External Electric Field Conductor (E-EFC), an external pulse transmitter, with included charger and the StimRouter Electrode Carrying Case. After the E-EFC is programmed, the E-EFC can be connected to the StimRouter Electrode through which it can deliver stimulation transcutaneously to the implanted lead receiver.

The provided text describes a 510(k) premarket notification for the StimRouter Neuromodulation System, arguing for its substantial equivalence to a previously cleared predicate device (K200482). The submission primarily focuses on comparing the technological characteristics of the new device (modified StimRouter) with the predicate, rather than presenting a study with specific acceptance criteria and performance data for a standalone algorithmic device.

Therefore, many of the requested details about acceptance criteria, specific device performance metrics, sample sizes for test/training sets, ground truth establishment, and multi-reader multi-case studies are not available in the provided document. The document describes a comparison study, not a standalone performance study as would be typical for an AI/ML device.

Here's a breakdown of what can be extracted and what is missing:

1. Table of Acceptance Criteria and Reported Device Performance

This information is not provided in the document as specific numerical acceptance criteria and corresponding reported device performance for an algorithm's classification or prediction capabilities. The document describes a comparison of technical characteristics between the modified StimRouter and its predicate, rather than reporting performance against predefined acceptance criteria.

The "Equivalency Assessment" column in the table on pages 5-6 indicates similarity to the predicate and states that differences do not affect safety and effectiveness of intended use. For example:

• EWD Electrical Signal Transmitter: "Similar. The E-EFC circuitry is functionally equivalent to the EPT circuitry. The differences do not affect safety and effectiveness of intended use."
• EWD Phase Duration: "The reduction in number of positive phase duration values (due to a simplified code base) does not affect safety and effectiveness of the intended use because minor adjustments can be made to other parameters to create therapeutic programs equivalent to those provided by the EPT."
• EWD Max Compliance Voltage: The E-EFC's maximum compliance voltage increased to 130V from the predicate's 100V. The assessment states: "This difference in hardware does not affect safety and effectiveness of the intended use."

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

Not applicable/Not provided. This document does not describe a study involving a test set of data for evaluating an AI/ML algorithm's performance. The "performance testing" mentioned on page 15 refers to electrical compatibility, wireless coexistence, biocompatibility, shipping, storage, shelf life, functional verification, usability, and software verification/validation—these are not related to a data-driven performance evaluation with a test set in the context of AI.

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

Not applicable/Not provided. As there is no described test set or ground truth establishment in the context of an AI/ML algorithm's performance, this information is not present.

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

Not applicable/Not provided.

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/Not provided. This submission is for a neuromodulation system, not an AI/ML diagnostic or assistive device that would involve human readers.

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

Not applicable/Not provided. The device described is a physical neuromodulation system, not a standalone algorithm. The software components are for controlling the device (CPS, MAPP app) and are evaluated through software verification and validation, not standalone diagnostic performance.

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

Not applicable/Not provided.

8. The sample size for the training set

Not applicable/Not provided. No AI/ML training is described.

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

Not applicable/Not provided.

Summary of the Study Performed (as described in the document):

The "study" described in the 510(k) submission is a comparison of technological characteristics and performance testing to demonstrate substantial equivalence to a predicate device (StimRouter Neuromodulation System cleared in K200482).

The core of the submission (pages 5-6) is a detailed table comparing the "Subject Device (Modified StimRouter)" to the "Predicate (StimRouter cleared in K200482)" across numerous technical attributes, including:

• Manufacturer, 510(k) number, Intended use (all identical)
• Implantable Lead and Lead Introducer Kit components (packaging, lead characteristics, introduction method, tools) - indicated as "No changes" or "Same"
• User Kit accessories (External Electrical Field Conductor (E-EFC) vs. External Pulse Transmitter (EPT), MAPP Smartphone Application vs. Patient Programmer)
• Clinician Kit accessories (Modified Clinician's Programming Software (CPS) vs. original CPS)

The "Equivalency Assessment" column justifies why any differences (e.g., changes in electronics, wireless protocol, charging port, integrated controls, maximum compliance voltage, or phase duration values) do not affect the safety and effectiveness of the intended use, arguing that similar functionality is maintained or improved.

Performance Testing Mentioned (page 15):

The document lists "Performance Testing" categories that the StimRouter Neuromodulation System was qualified through:

• Electrical compatibility and safety
• Wireless coexistence
• Biocompatibility Testing
• Shipping and storage
• Shelf life
• Functional Verification
• Usability
• Software Verification and Validation Testing

These tests are standard for medical devices and demonstrate the device's adherence to relevant standards and its functional capabilities, rather than an AI/ML algorithm's data-driven performance. The document concludes that based on these comparisons and performance tests, the modified StimRouter is substantially equivalent to its predicate and does not raise new safety or effectiveness concerns.

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The Neuspera Neurostimulation System (NNS) is indicated for pain management in adults who have severe intractable chronic pain of peripheral nerve origin, as the sole mitigating agent or as an adjunct to other modes of therapy used in a multidisciplinary approach. The system is not intended to treat pain in the craniofacial region.

The Neuspera Neurostimulation System (NNS) is also used for trial stimulation (no longer than 30 days) to determine efficacy before recommendation for a permanent (long term) implant.

The Neuspera Neurostimulation System is used for peripheral nerve stimulation to provide therapeutic relief for chronic, intractable pain of peripheral nerve origin. The System consists of an implantable pulse generator (IPG), electrode array, surgical implant tools, wireless worn transmitter, clinician programmer and a patient controller. The implantable pulse generator is a miniature implanted neurostimulator, powered by an externally worn wireless transmitter device which contains a rechargeable battery.

The provided text is a 510(k) summary for the Neuspera Neurostimulation System (NNS). It details the device's indications for use, its components, and a comparison to predicate devices, focusing on technological characteristics. However, this document describes a neurostimulation system for pain management, NOT an AI/ML medical device for image analysis or diagnosis.

Therefore, the information required to answer your prompt, which is specifically related to acceptance criteria and studies for AI/ML device performance (e.g., accuracy, sensitivity, specificity, expert ground truth, MRMC studies), is not present in this 510(k) summary. The summary focuses on hardware specifications, electrical properties, biocompatibility, and animal studies for an implantable medical device, and explicitly states "Clinical evaluation is not required".

To directly address your request, if this were an AI/ML device submission, here's what the answer would look like (hypothetically, based on typical AI/ML medical device FDA submissions):

Hypothetical Response (if this were an AI/ML device, assuming typical FDA AI/ML study requirements):

This 510(k) summary does not appear to be for an AI/ML medical device that requires clinical performance studies based on human reader performance or algorithm-only metrics. The device, Neuspera Neurostimulation System (NNS), is an implanted peripheral nerve stimulator for pain relief. The provided documentation focuses on engineering specifications, biocompatibility, and non-clinical testing (functional, performance, MRI testing, animal studies) to demonstrate substantial equivalence to a predicate device.

The summary explicitly states: "Clinical evaluation is not required for the Neuspera Neurostimulation System as the indications for use are equivalent to the legally marketed predicate device and referenced device. These types of devices, including versions of the legally marketed predicate device, have been on the market for many years with a proven safety and efficacy for the use of the device. Therefore, Neuspera determined that bench and non-clinical testing are sufficient to demonstrate that the Neuspera Neurostimulation System is as safe and effective as the predicate device."

Therefore, the requested information regarding acceptance criteria, performance metrics (like sensitivity, specificity), data provenance, expert ground truth establishment, adjudication methods, MRMC studies, or standalone algorithm performance, which are typical for AI/ML diagnostic or prognostic devices, is not applicable or available in this specific 510(k) submission for the Neuspera Neurostimulation System.

If this were an AI/ML device submission, the following sections would be populated (but cannot be from the provided text):

1. Table of acceptance criteria and reported device performance:
   (Hypothetical example for an AI/ML device)

2. Sample size used for the test set and the data provenance:
   (Hypothetical example for an AI/ML device)

   • Test Set Sample Size: E.g., 500 cases (e.g., medical images).
   • Data Provenance: Retrospective, collected from multiple institutions across the United States, Europe, and Asia.

3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts:
   (Hypothetical example for an AI/ML device)

   • Number of Experts: E.g., 3 independent board-certified radiologists.
   • Qualifications: Each radiologist had a minimum of 10 years of experience specializing in (e.g., thoracic imaging) and were blinded to the device's output.

4. Adjudication method for the test set:
   (Hypothetical example for an AI/ML device)

   • Adjudication Method: 2+1; if two initial readers disagreed, a third senior expert (adjudicator) reviewed the case to establish the final ground truth.

5. If a Multi-Reader Multi-Case (MRMC) comparative effectiveness study was done:
   (Hypothetical example for an AI/ML device)

   • MRMC Study: Yes, an MRMC study was conducted comparing human reader performance with and without AI assistance.
   • Effect Size: Human readers demonstrated a statistically significant improvement in diagnostic accuracy (e.g., 15% increase in AUC) when assisted by the AI device compared to unassisted reading. The sensitivity increased by X% and specificity by Y%.

6. If a standalone (i.e., algorithm only without human-in-the-loop performance) was done:
   (Hypothetical example for an AI/ML device)

   • Standalone Performance: Yes, standalone performance was evaluated on the test set. The algorithm achieved a sensitivity of 92.5% and a specificity of 85.1%.

7. The type of ground truth used:
   (Hypothetical example for an AI/ML device)

   • Ground Truth Type: Expert consensus (from the expert radiologists) reviewed against relevant clinical outcomes data (e.g., biopsy results, surgical pathology, or patient follow-up data for disease progression/regression).

8. The sample size for the training set:
   (Hypothetical example for an AI/ML device)

   • Training Set Sample Size: E.g., 10,000 cases.

9. How the ground truth for the training set was established:
   (Hypothetical example for an AI/ML device)

   • Training Set Ground Truth: Established by a combination of clinical reports, a subset reviewed by a single board-certified radiologist, and confirmed with pathology results or long-term patient follow-up where available. Automated methods (e.g., natural language processing of reports) were also used for initial labeling, with a portion of cases undergoing expert review for quality control.

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