EyeBOX

The EyeBOX Model OCL 02 is an eye-tracking device intended as an adjunctive tool for diagnosing concussion (mTBI) in individuals aged 5 to 67 within one week of head injury. It operates by capturing high-frequency gaze data during a visual tracking task and employs a proprietary algorithm to analyze these movements, generating a 'BOX score' to aid in identifying ocular motor patterns potentially associated with concussion.

The EyeBOX, Model OCL 02, is an eye-tracking device designed as an aid in the diagnosis of concussion, also referred to as mild traumatic brain injury (mTBI). It is intended for use with patients aged 5 through 67 years within one week of a head injury, functioning as a supplementary tool to a standard neurological assessment. The system comprises a host PC with an integrated touchscreen, an eye-tracking camera, an LCD stimulus screen for presenting visual tasks, and a head-stabilizing rest (chin and forehead rest) for the patient. The core of its functionality lies in a data processing algorithm engineered to detect subtle changes in eye movements that may result from a concussion. During the assessment, which takes approximately 4 minutes, the patient follows a video that moves around the perimeter of the LCD monitor. A high-speed near-infrared (IR) camera records the patient's gaze positions 500 times per second. This collected data is then automatically post-processed to produce a 'BOX score.' A negative EyeBOX classification suggests eye movements consistent with the absence of concussion, while a positive classification indicates eye movements that could be present in individuals both with and without concussion. The Model OCL 02 is a tabletop version with WiFi capability, designed for seated patients only, and includes software changes for device licensing. Crucially, it retains the same eye-tracking camera, optical path, and the fundamental EyeBOX algorithm for data processing and BOX score generation as its predicate model.

AI Regulatory Concerns

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Jev reads the full FDA letter and returns calibrated concern probabilities per regulatory category. These are AI estimates, not regulatory advice.

• Q1. What is the sensitivity and specificity of the EyeBOX (Model OCL 02) in differentiating between patients with and without concussion, particularly in the 5-67 age range specified?; How does the 'BOX score' quantitatively represent eye movement changes, and what specific kinematic parameters or patterns does the algorithm analyze?; Could the EyeBOX be adapted or validated for assessing concussion severity or tracking recovery progress over time, beyond the initial one-week post-injury window?; What impact, if any, do pre-existing ophthalmological conditions, medications affecting eye movement, or patient fatigue have on the accuracy of the EyeBOX assessment?; How does the performance of the EyeBOX compare to other objective concussion assessment tools (e.g., neuroimaging, other biomarkers) in terms of diagnostic accuracy and clinical workflow integration?