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The balanceback ™ Mobile Intuitive VNG device is intended for recording, viewing, and analyzing eye movements in support of identifying balance disorders in human subjects. The Mobile iVNG is intended for use only by trained physicians or clinicians in an appropriate doctor's office or health care facility. This device provides no diagnoses nor does it provide diagnostic recommendations.

Eye movements have long been known to provide important clues to the identification and location of balance disorders. Physicians and other clinicians employ the VNG system to conduct a set of established, well-known and well-documented eye movement tests. The results of the VNG eye movement tests are combined with other clinical information so that the attending clinicians may render findings on the health of the patient's primary balance sensors and related balance processing system.

Using the Mobile VNG, clinicians guide patients through a series of eye movement tests. A testing battery (i.e., an examination) typically consists of all eight tests supported by the VNG: caloric, positional, Hallpike, gaze, saccades, smooth pursuit, optokinetic, and highfrequency head shake. The attending physician may select one, several, or all tests, and has full flexibility in determining the sequence of the tests to be performed.

The patient under examination wears the iVNG goggles contain one IR light source and one IR camera for each eye. The IR video cameras sense the infrared signal reflected from the eyes and create a continuous monochromatic (grayscale) video motion picture stream of the eyes. The infrared signal is used because the human eye cannot detect the presence of the infrared signal and therefore this illumination does not create perturbations in measurements of eye movement. Measurements may therefore be made both in the presence and in the absence of visible light, thus providing clinicians with maximum flexibility for making measurements in a wide variety of situations.

The video stream from the IR cameras is digitized and transferred into the memory of the digital computer where signal processing software tracks, records, analyzes and displays eve movements for the attending clinician. The Mobile iVNG system makes appropriate calculations and presents reports of well-known and well-documented parameters associated with each eye movement test.

Some eye tests require the patient to follow a visual stimulus. A projector, controlled by the iVNG hardware, displays stimuli on any light-colored surface, such as a wall or projection screen. The goggles and Mobile iVNG software record the patient's eve movements while he/she follows the stimulus.

The provided document {0} does not contain explicit acceptance criteria and corresponding performance data for the balanceback™ Mobile VNG system as typically found in a clinical study report. Instead, it describes "Testing" related to verifying system specifications and conformance to recognized electrical and safety standards. There is no mention of a clinical study assessing the device's accuracy in identifying balance disorders or the specifics of algorithmic performance.

Therefore, the following information is based on the provided text, acknowledging the absence of direct answers to several points usually found in performance studies.

Acceptance Criteria and Reported Device Performance

Given the nature of the provided document, the "acceptance criteria" are related to system specifications and compliance with recognized standards, rather than direct clinical performance metrics (like sensitivity, specificity, accuracy for identifying balance disorders). The document states:

Note: The document states, "The device will not be marketed or sold until it has been certified to pass these tests." The FDA's 510(k) clearance letter (pages {2} and {3}) indicates that the device was cleared for marketing, suggesting these criteria were met.

Study Information (Based on available text):

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

   • The document does not specify any sample size for a clinical or performance test set regarding the device's ability to "record, view, and analyze eye movements in support of identifying balance disorders."
   • There is no information on data provenance (e.g., country of origin, retrospective or prospective) as no such study is detailed.

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

   • Not applicable/Not provided. The document does not describe a study where ground truth was established by experts for a test set related to balance disorder identification.

3. Adjudication method for the test set:

   • Not applicable/Not provided. No clinical test set or adjudication method is mentioned.

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 such study is described. The device's indications for use explicitly state, "This device provides no diagnoses nor does it provide diagnostic recommendations." This implies the device is a tool for clinicians, not an AI system that would assist in diagnosis or require an MRMC study for improved human reader performance.

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

   • No standalone algorithm performance study is described. The device is intended to be used by trained physicians or clinicians.

6. The type of ground truth used:

   • Not applicable/Not provided. The document focuses on technical verification and substantial equivalence to a predicate device, not on validating its diagnostic accuracy against a clinical ground truth. The device "provides no diagnoses nor does it provide diagnostic recommendations," reinforcing that its purpose is to provide data for the clinician's interpretation.

7. The sample size for the training set:

   • Not applicable/Not provided. The document does not describe any machine learning or AI components that would require a "training set" in the context of diagnostic algorithms.

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

   • Not applicable/Not provided. As there is no mention of a training set for an AI/ML algorithm, the method for establishing its ground truth is not discussed.

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