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The EyeBOX is intended to measure and analyze eye movements as an aid in the diagnosis of concussion, also known as mild traumatic brain injury (mTBI), within one week of head injury in patients 5 through 67 years of age in conjunction with a standard neurological assessment of concussion.

A negative EyeBOX classification may correspond to eye movement that is consistent with a lack of concussion.

A positive EyeBOX classification corresponds to eye movement that may be present in both patients with or without concussion.

Oculogica's EyeBOX model EBX-4 uses eye-tracking technology and a data processing algorithm to detect subtle changes in eye movements resulting from concussion. The eye tracking task takes about 4 minutes to complete and involves watching a video move around the perimeter of an LCD monitor positioned in front of the patient while a high speed near-infrared (IR) camera, positioned just below the screen, records eye movements. The data are then analyzed to produce one or more outcome measures.

The patient sits in front of the device with their head secured in a chinrest about 40 cm from the middle of the display. A face tracking camera, positioned above the screen, assists the patient and operator with proper placement of the patient by providing a second set of eye coordinates in three-dimensional space to calculate the patient's distance from the screen and the patient face's position relative to the display.

The device contains a rechargeable battery, which makes it possible to use without a direct connection to an active power source.

The device has Wi-Fi and Ethernet capabilities, which optionally can provide the user with the ability to send reports to a remote printer and/or file storage location.

No clinical data was collected to prove the device meets acceptance criteria. The submission states, "No new clinical data were collected."

The document states that the EyeBOX Model EBX-4 is substantially equivalent to its predicate device, EyeBOX Model OCL 02.5. The rationale for this substantial equivalence is based on the fact that the EBX-4 has the same intended use, similar technological characteristics, and principles of operation as the predicate device. The changes implemented in the EBX-4 (e.g., removal of integrated headrest/chinrest, updated camera system, electronic component changes) are considered minor and do not raise new questions of safety or effectiveness.

To support this claim of substantial equivalence, Oculogica, Inc. performed performance testing rather than clinical efficacy studies. These tests focused on verifying the functionality and safety of the modified hardware and software.

Here's a breakdown of the requested information based on the provided document:

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

The document does not provide a specific table of acceptance criteria with corresponding performance metrics from a clinical study. Instead, the acceptance criteria appear to be addressed through a series of verification and validation activities showing that the device "functioned as intended" and that changes did not "adversely impact device performance."

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

• Test Set Sample Size: Not applicable. No new clinical data or test set was used for clinical performance evaluation. The performance testing focused on hardware and software verification.
• Data Provenance: Not applicable. No new clinical data was collected.

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. No clinical test set with human-established ground truth was used for this submission, as it relied on substantial equivalence and performance testing.

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

• Not applicable. No clinical test set was used.

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

• No MRMC comparative effectiveness study was done. The submission explicitly states, "No new clinical data were collected."

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

• No standalone clinical performance study was done for this submission. The device is referred to as an "aid in the diagnosis of concussion... in conjunction with a standard neurological assessment of concussion," implying a human-in-the-loop use case. However, its performance was assessed through technical verification tests, not a clinical trial.

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

• For the technical performance testing, the ground truth would be defined by engineering specifications and expected functional behavior, verified against established standards (e.g., IEC standards for electrical safety, internal specifications for eye-tracking accuracy).
• For the original predicate device, the ground truth for clinical validation would have been established through clinical assessments of concussion (e.g., standard neurological assessments, potentially outcomes data or expert diagnosis), but details of the predicate device's clinical validation are not provided in this specific document. This submission does not provide information about the ground truth for the predicate device.

8. The sample size for the training set

• Not applicable. The document describes a device with "a data processing algorithm," but it does not specify if or how a machine learning model was trained with a dataset. The emphasis is on the EyeBOX system's "principles of operation" remaining unchanged from the predicate device, suggesting the core algorithm (and any associated training) was already established.

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

• Not applicable. Information regarding a training set or its ground truth establishment is not provided in this 510(k) summary, as the submission relies on substantial equivalence and verification testing of hardware/software changes.

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