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The Checkpoint Guardian Intraoperative Lead is a single-use, sterile medical device accessory intended to be used with Checkpoint Stimulators to provide electrical stimulation of exposed motor nerves or muscle tissue to identify nerves and to test nerve and muscle excitability.

The Checkpoint Guardian Intraoperative Leads are single-patient disposable accessories for providing stimulus to a targeted nerve. The accessories are intended for use with any device from the Checkpoint Stimulator/Locator Family. The lead electrode is intended to wrap around nerves that have been surgically exposed, allowing the surgeons to provide hands free stimulus to a targeted nerve.

The provided text describes a 510(k) premarket notification for the Checkpoint Guardian Intraoperative Lead, a medical device accessory. The submission focuses on demonstrating substantial equivalence to a predicate device, rather than proving a specific performance level of the new device itself through a diagnostic study with acceptance criteria.

Therefore, the requested information regarding acceptance criteria and a study proving the device meets these criteria in the context of diagnostic performance (e.g., sensitivity, specificity, accuracy) is not available from this document. The document primarily details nonclinical testing to ensure the device's safety and functionality are equivalent to the predicate.

Here's an analysis based on the information provided, recognizing that it doesn't fit the typical diagnostic device evaluation framework:

1. A table of acceptance criteria and the reported device performance

The document does not present acceptance criteria in terms of diagnostic performance metrics (e.g., sensitivity, specificity, or accuracy) for the Checkpoint Guardian Intraoperative Lead. Instead, testing focused on engineering, safety, and compatibility standards. The "performance" is reported as meeting these standards.

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

The document refers to "a battery of bench and user tests" and "Design Verification Testing (DVT)" but does not specify the sample size (e.g., number of leads tested) for these tests. Data provenance, such as country of origin or whether it was retrospective/prospective, is not mentioned. It is implied these were internal, prospective engineering tests.

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)

This is not applicable as the study was not a diagnostic study requiring expert ground truth for interpretation. The "ground truth" here would be established by validated engineering specifications and international standards, and results would be interpreted by qualified engineers and compliance personnel.

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

Not applicable, as this was not a diagnostic study involving human interpretation needing adjudication.

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 device is an accessory for intraoperative nerve stimulation, not an AI-powered diagnostic or assistive tool.

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

Not applicable. This is a physical medical device accessory, not an algorithm.

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

The "ground truth" for the nonclinical testing described was adherence to established industry standards (e.g., IEC 60601 series for electrical safety, ISO 10993 series for biocompatibility), internal design specifications, and functional performance requirements.

8. The sample size for the training set

Not applicable. This is a physical medical device, not a machine learning model, so there is no training set.

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

Not applicable.

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The Checkpoint® Head & Neck is a single-use device intended to provide electrical stimulation of exposed motor nerves or muscle tissue to locate and identify nerves and to test nerve and muscle excitability.

The Checkpoint Head & Neck is a small handheld device used by a surgeon to deliver electrical stimulation intraoperatively to locate and identify nerves, and to test nerve integrity and muscle excitability. These are sterile disposable devices designed to use with one-handed control.

The device uses a stimulating probe that is attached to the distal end and a cable attached to a needle return electrode on the proximal end. The microcontroller in the device includes embedded firmware that controls the function of the Stimulator. The firmware cannot be modified by the user. The stimulator is powered by internal batteries that are not user replaceable, thereby minimizing the potential for re-use.

The provided text is a 510(k) summary for the NDI Medical, LLC Checkpoint® Head & Neck surgical nerve stimulator. It focuses on demonstrating substantial equivalence to a predicate device rather than presenting a detailed clinical study with acceptance criteria and performance metrics typical of a novel device approval process.

Therefore, the requested information regarding acceptance criteria, reported device performance, sample sizes, ground truth establishment, expert qualifications, adjudication methods, multi-reader multi-case studies, and standalone performance is not explicitly available within the provided document.

Here's a breakdown of what can be extracted regarding testing and related information, and where the information is missing:

Summary of Device Acceptance Criteria and Performance (Based on Available Information)

As this is a 510(k) submission for a device modification, the "acceptance criteria" are primarily based on demonstrating substantial equivalence to the predicate device and meeting general safety and performance standards for surgical nerve stimulators. The document does not provide specific quantitative acceptance criteria (e.g., minimum sensitivity, specificity, or accuracy) for clinical performance in the way one might expect for an AI diagnostic device. Instead, "performance" is demonstrated through various engineering and safety tests.

Missing Information and Why it's Not Present:

The provided document is a 510(k) summary, which is a premarket notification to the FDA. For devices seeking 510(k) clearance, the primary goal is often to demonstrate "substantial equivalence" to a legally marketed predicate device, rather than proving the safety and effectiveness from scratch through extensive clinical studies with detailed performance metrics.

For this specific device, a surgical nerve stimulator/locator, the demonstration of substantial equivalence relies heavily on:

• Same intended use.
• Similar technological characteristics.
• Biocompatibility, electrical safety, and EMC testing to ensure it meets general performance standards for such devices.

Therefore, the document does not contain the following information:

1. Sample size used for the test set and the data provenance: Clinical performance data (like sensitivity/specificity studies) with designated test sets is not detailed as the primary path for clearance here is substantial equivalence based on engineering and safety testing.
2. Number of experts used to establish the ground truth for the test set and the qualifications of those experts: Not applicable as a formal test set with expert-established ground truth is not described.
3. Adjudication method (e.g., 2+1, 3+1, none) for the test set: Not applicable.
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: This is a medical device, not an AI diagnostic imaging algorithm. MRMC studies are not relevant here.
5. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done: Not applicable, as this is a human-operated surgical tool.
6. The type of ground truth used (expert consensus, pathology, outcomes data, etc): Not applicable for the type of testing described in this 510(k). The "ground truth" for electrical stimulation devices often relates to known electrical properties and physiological responses, tested in a laboratory or preclinical setting.
7. The sample size for the training set: Not applicable, this is not an AI/ML device requiring a training set.
8. How the ground truth for the training set was established: Not applicable.

In conclusion: The provided 510(k) summary focuses on the technical and regulatory aspects of device modification and substantial equivalence. It confirms that "biocompatibility testing, electrical testing (safety and electromagnetic compatibility), as well as design verification and validation testing" were performed, but it does not provide the specific quantitative results or the detailed clinical study data that you would expect for a diagnostic AI device requiring the specified acceptance criteria and ground truth information.

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The Checkpoint® is a single-use sterile device intended to provide electrical stimulation of exposed motor nerves or muscle tissue to locate and identify nerves and to test nerve and muscle excitability. The Checkpoint is available for prescription use only.

The Checkpoint® is a small handheld device used by a surgeon to deliver electrical stimulation intraoperatively to test nerve integrity and muscle excitability. This is a sterile disposable device designed to be simple to use with one-handed control.

This 510(k) summary for the NDI Medical Checkpoint® Surgical Nerve Stimulator/Locator does not describe a study involving acceptance criteria and device performance in the manner typically associated with clinical trials or AI algorithm validation.

Instead, the document details a submission for a substantial equivalence determination for a modified version of an existing device (K061365 - Checkpoint Surgical Nerve Stimulator/Locator). The focus is on demonstrating that the modified device is as safe and effective as the predicate device, primarily through technological comparison and standard performance testing (biocompatibility, electrical safety).

Therefore, I cannot provide the requested information in the format you specified, as the provided document does not contain the elements of a study proving device performance against acceptance criteria in the context of, for example, a diagnostic accuracy study or an AI algorithm's performance.

Here's an analysis based on the information available in the document, explaining why the requested details are not present:

1. A table of acceptance criteria and the reported device performance:

   • Not applicable in the provided context. The document presents a table comparing "Feature/Characteristic" between the predicate and modified device, showing "Yes" for each characteristic, indicating technological equivalence rather than performance against specific clinical acceptance criteria. Performance testing mentioned (biocompatibility, electrical testing, design verification and validation) are general categories, not detailed outcome measures with thresholds.

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

   • Not applicable. There is no mention of a "test set" in the context of clinical data for performance evaluation in this document. The assessment is based on technological characteristics and general performance testing (electrical, biocompatibility).

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. There is no "ground truth" to be established by experts in this type of submission, as it's not a diagnostic or decision-support device requiring interpretation of complex data by human experts.

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

   • Not applicable. No test set requiring 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:

   • Not applicable. This device is a surgical nerve stimulator/locator, not an AI-powered diagnostic or assistive tool. MRMC studies are irrelevant here.

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

   • Not applicable. This device is a physical, handheld surgical tool. There is no "algorithm only" performance to evaluate.

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

   • Not applicable. The "ground truth" for demonstrating substantial equivalence here relies on documented design specifications, manufacturing controls, and standardized performance tests (like electrical conductivity, biocompatibility) rather than clinical outcome measures requiring expert comparison.

8. The sample size for the training set:

   • Not applicable. No training set is mentioned as this is not an AI/machine learning device.

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

   • Not applicable. No training set is mentioned.

In summary: The provided 510(k) summary focuses on demonstrating "substantial equivalence" of a modified medical device to a legally marketed predicate device. This process typically involves:

• Comparison of intended use and indications for use. (Explicitly stated as equivalent)
• Comparison of technological characteristics. (Provided in a table)
• Assessment of performance testing. (Mentioned as "biocompatibility testing, electrical testing (safety and electromagnetic compatibility), as well as design verification and validation testing.") These are typically engineering and materials tests rather than clinical performance studies against specific acceptance criteria.

The document does not describe a clinical study with a test set, ground truth acquisition, expert involvement, or statistical performance metrics that would be typically found in an AI/diagnostic device submission.

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The Checkpoint™ is a single-use device intended to provide electrical stimulation of exposed motor nerves or muscle tissue to locate and identify nerves and to test nerve and muscle excitability.

The Checkpoint™ is a small handheld device used by a surgeon to deliver electrical stimulation intraoperatively to test nerve integrity and muscle excitability. This is a sterile disposable device designed to be simple to use with one-handed control.

The NDI Medical Checkpoint™ is a surgical nerve stimulator/locator. The provided text from the 510(k) submission describes the device, its intended use, and substantial equivalence to predicate devices. However, it does not contain details about specific acceptance criteria or an analytical study comparing the device's performance against such criteria.

The "PERFORMANCE TESTING" section states that testing included "biocompatibility testing, electrical testing (safety and electromagnetic compatibility), as well as design verification and validation testing." This is a general statement about the types of tests conducted, but it doesn't provide specific device performance metrics or the acceptance criteria for those metrics.

Therefore, many of the requested details about acceptance criteria and study design cannot be extracted from the provided text.

Here's an attempt to answer the questions based on the available information, noting where information is missing:

1. Table of Acceptance Criteria and Reported Device Performance

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

• Sample size for test set: Not provided.
• Data provenance: Not provided. The document mentions general testing categories but does not specify the origin or nature of the data used for these tests.

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

• Number of experts: Not provided.
• Qualifications of experts: Not provided.

4. Adjudication method for the test set

• Adjudication method: 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

• MRMC study done: No. This device is a surgical nerve stimulator/locator, not an AI-powered diagnostic imaging device that would typically involve human "readers" or AI assistance in interpretation. The document does not mention any AI component.
• Effect size: Not applicable, as there was no MRMC study or AI component mentioned.

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

• Standalone study done: Not applicable. The device itself is a "small handheld device used by a surgeon to deliver electrical stimulation intraoperatively." Its performance is inherently linked to the surgeon's use, and there's no mention of an "algorithm only" component in the context of its primary function.

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

• Type of ground truth: Not provided. For a device like this, ground truth would likely be established through objective measurements (e.g., electrical output, duration, mechanical integrity) and potentially clinical observation of its ability to stimulate nerves/muscles as expected. However, the document does not specify.

8. The sample size for the training set

• Sample size for training set: Not applicable. This document describes a physical medical device, not a machine learning algorithm that requires a training set.

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

• How ground truth was established: Not applicable, as there is no training set for this type of device.

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