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The CERSR® Electromyography System is intended to be used by properly trained technicians and/or physicians in clinical settings. The CERSR® Electromyography System is indicated for use to monitor and display the bioelectric signals produced by muscles to aid in the diagnosis and prognosis of muscular disease or dysfunction.

The CERSR® is an Electromyography System. The CERSR® Electromyography System is specifically designed for a real-time recording of muscle electrophysiology. The CERSR® Electromyography System allows for a real-time recording from multiple locations by applying an array of surface electrodes over the anatomical region of interest. Each electrode is connected to its own channel with a preamplifier, amplifier, buffers and filters. The CERSR® Electromyography System produces a user display of the myoelectric signals. These recordings may be viewed in one of three standard formats, as a typical waveform, RMS display or a frequency spectral analysis plot. The system consists of the following components: 1) a system cart comprised of a CPU: 2) a mouse and keyboard; 3) monitor; 4) printer; 5) two buffer amplifiers; 6) power distribution box. which contains a filter buffer box; 7) isolation transformer; 8) two power supplies; and 9) a static ground system. The CERSR® system contains software and includes a disposable electrode array. Additional accessories provided with the system include a goniometer, a flexible transparent ruler, and two 3-pound weights.

The provided text is a 510(k) Summary for the SpineMatrix CERSR® Electromyography System. It describes a modification to an existing device, emphasizing that the changes are primarily hardware upgrades for user convenience and system robustness, with no new features or capabilities.

This submission does not detail a study proving the device meets acceptance criteria in the context of an AI/ML medical device, as it describes an electromyography system, not an AI system. Therefore, most of the requested information regarding acceptance criteria, performance metrics, ground truth establishment, sample sizes for training/test sets, expert adjudication, or MRMC studies is not applicable to this document. The term "acceptance criteria" here refers to standard medical device safety and performance testing, not AI model performance.

Below is an attempt to address the request based on the limited, relevant information available in the provided text, and explicitly state what information is not present because it pertains to an AI/ML context not applicable here.

Acceptance Criteria and Device Performance

The "acceptance criteria" for this device, as demonstrated in the 510(k) submission, are primarily established through compliance with recognized medical device standards and non-clinical performance testing to ensure safety, electromagnetic compatibility, biocompatibility, and software functionality. The device is a direct modification of a previously cleared device, and the demonstration of "substantial equivalence" is the primary regulatory pathway.

1. Table of Acceptance Criteria and the Reported Device Performance

Summary of Device Performance: The non-clinical performance testing demonstrated that the device performs as safely and effectively as the legally marketed predicate device. The changes did not raise any new questions of safety or effectiveness.

2. Sample size used for the test set and the data provenance:
Not applicable. This is not an AI/ML device that uses a "test set" in the sense of a dataset for validating model performance. The testing involved compliance with engineering standards and physical/electrical/software testing, not data-driven performance.

3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts:
Not applicable. There is no "ground truth" established by experts in the context of an AI/ML model for this device. The ground truth for this medical device's performance is compliance with established electrical, safety, and biocompatibility standards, and functional verification of the software/hardware.

4. Adjudication method (e.g. 2+1, 3+1, none) for the test set:
Not applicable. No adjudication method for a test set is relevant to this type of device submission.

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-assisted device, and no MRMC study was conducted or required.

6. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done:
Not applicable. There is no standalone algorithm or AI model in this device. It is an electromyography system that monitors and displays bioelectric signals directly from muscles.

7. The type of ground truth used (expert consensus, pathology, outcomes data, etc.):
The "ground truth" for this device's validation is the adherence to and successful performance against established engineering, safety, and biocompatibility standards (e.g., IEC, ISO, ASTM). The efficacy is presumed to be equivalent to the predicate device because the fundamental technology and intended use remain unchanged.

8. The sample size for the training set:
Not applicable. As this is not an AI/ML device, there is no "training set."

9. How the ground truth for the training set was established:
Not applicable. No training set exists for this device.

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The CERSR™ Electromyography System is used to monitor and display the bioelectric signals produced by muscles to aid in the diagnosis and prognosis of muscular disease or dysfunction.

CERSR™ is an electromyography system. CERSR™ is substantially equivalent to legally marketed predicate electromyographic systems. Like most electromyographic systems. CERSR™ allows for the monitoring and displaying of the bioelectrical signals generated by muscles. CERSR™ is specifically designed for a real-time recording of muscle electrophysiology. Most electromyographic systems have at most five channels for monitoring and displaying the activity of several muscle groups simultaneously. The CERSR™ reduces the problems associated with too few channels and allows for a real-time recording from multiple locations by applying an array of surface electrodes over the anatomical region of interest. Each electrode is connected to its own channel with preamplifier, amplifier, buffers and filters. The CERSR™ produces a user display of the myoelectric signals. These recordings may viewed in one of three standard formats, as a typical waveform, RMS display or as a frequency spectral analysis plot.

This document is a 510(k) summary for the CERSR™ Electromyography System from 1999. It primarily focuses on demonstrating substantial equivalence to predicate devices rather than presenting a detailed study of the device's performance against specific acceptance criteria.

Based on the provided text, there is no information that directly describes acceptance criteria or a study proving the device meets those criteria in a quantitative sense. The document asserts substantial equivalence based on the device's technological characteristics and its ability to perform core functions similar to existing devices.

Therefore, many of the requested details cannot be extracted from this summary.

Here's a breakdown of what can and cannot be answered:

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

• Cannot be provided. The document does not define specific acceptance criteria (e.g., sensitivity, specificity, accuracy, signal-to-noise ratio requirements) or report quantitative performance metrics for the CERSR™ system in a study format. The claim is one of "substantial equivalence" in function and technological characteristics.

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

• Cannot be provided. No test set or related data is discussed.

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)

• Cannot be provided. No ground truth establishment activity is mentioned for a test set.

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

• Cannot be provided. No test set or adjudication process is mentioned.

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

• Cannot be provided. This device is an electromyography system, not an AI-assisted diagnostic tool for "human readers" in the context of MRMC studies typical for imaging or AI. No MRMC study is mentioned.

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

• Cannot be provided. This is an EMG system designed for monitoring and displaying bioelectric signals, inherently used by a human clinician. It does not operate as a standalone algorithm in the sense of AI or automated diagnosis.

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

• Cannot be provided. No ground truth for performance evaluation is mentioned. The device's "ground truth" functionally lies in its ability to accurately record muscle bioelectric signals, but no formal validation study is presented here.

8. The sample size for the training set

• Cannot be provided. There's no mention of a "training set" as this predates (or doesn't involve) machine learning/AI development in this context. The device's design is based on established electromyography principles.

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

• Cannot be provided. As there's no training set, this question is not applicable.

Summary based on the document:

The 510(k) summary for the CERSR™ Electromyography System functions as a regulatory submission arguing for "substantial equivalence" to existing, legally marketed predicate devices. It asserts that the CERSR™ shares the same indications for use and technological characteristics (monitoring and displaying bioelectric signals in standard formats) as these predicates. The approval letter from the FDA (dated August 27, 1999) confirms this determination of substantial equivalence, allowing the device to be marketed.

This type of submission typically relies on comparing the new device's design, materials, and intended use against those of already approved devices, rather than conducting a de novo performance study against pre-defined acceptance criteria for a new clinical claim.

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