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The intended use of the ETG-4100 is the measurement of relative levels of cerebral deoxyhemoglobin and oxyhemoglobin.

The Optical Topography System ETG-4100 is a device that measures and displays relative changes of oxy-hemoglobin, deoxy-hemoglobin and total hemoglobin in the cerebral cortex at multiple points on the head. The ETG-4100 system emits near-infrared light (670-1300mm) of two different wavelengths (695nm and 830nm) that can efficiently penetrate the skull where the light is scattered, absorbed by hemoglobin and unabsorbed light is reflected back.

Here's a breakdown of the acceptance criteria and the study information for the Hitachi Healthcare Americas Optical Topography System ETG-4100 (K172492), based on the provided document:

The document (510(k) summary) focuses on demonstrating substantial equivalence to a predicate device (Optical Topography System ETG-4000 (K042501)), rather than defining specific, quantitative acceptance criteria for device performance based on clinical outcomes. The "acceptance criteria" in this context refer to the successful demonstration that the new device is as safe and effective as the predicate device, despite minor differences.

1. Table of Acceptance Criteria (Substantial Equivalence) and Reported Device Performance

Given the nature of a 510(k) for substantial equivalence, the "acceptance criteria" are tied to comparing the new device's characteristics and performance to the predicate device. The device "performance" here refers to demonstrating that differences do not negatively impact safety and effectiveness.

Study Information

The provided document describes a "Summary of Non-Clinical Testing" rather than a traditional clinical study with human subjects for efficacy or diagnostic performance. This is typical for a 510(k) submission focused on demonstrating substantial equivalence to a legally marketed predicate device.

Here's what can be inferred about the "study" (non-clinical testing):

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

   • Test Set Sample Size: Not explicitly stated in terms of number of patients or cases. The "signal stability testing" would likely involve laboratory or bench testing rather than patient data.
   • Data Provenance: Not specified, but given it's non-clinical testing, it would be laboratory or bench data, not human subject data from a specific country or collected retrospectively/prospectively.

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

   • Not applicable for this type of non-clinical, substantial equivalence testing. Ground truth, in the sense of expert review for diagnostic accuracy, is not described in a 510(k) summary for a device like an oximeter, especially when arguing substantial equivalence through technical characteristics and signal stability.

3. Adjudication method for the test set:

   • Not applicable. There is no mention of expert adjudication as there were no patient-level performance evaluations requiring such a process.

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, an MRMC study was not done. This device is an oximeter, not an AI-assisted diagnostic imaging tool with human readers. The document does not describe any AI component or human-in-the-loop performance.

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

   • Yes, in essence, standalone non-clinical testing was performed. The "signal stability testing" and "software verification and validation" were evaluations of the device's inherent performance and its new features in an isolated, technical context, without human interaction in a diagnostic or clinical decision-making loop. The device directly measures and displays hemoglobin levels; it doesn't process images or inputs for human interpretation in the way AI radiology devices do.

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

   • For signal stability testing, the "ground truth" would be established physical or electrical parameters or established measurements from the predicate device and calibration standards. It is not biological "ground truth" like pathology.
   • For software verification and validation, the ground truth would be the documented functional requirements and design specifications of the software, confirming that the software behaves as intended.

7. The sample size for the training set:

   • Not applicable. This document describes non-clinical testing for a medical device that measures physiological parameters, not an AI/ML model that is 'trained' on data.

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

   • Not applicable. As above, there is no AI/ML training set mentioned or implied.

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