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The GDx is a confocal polarimetric scanning laser ophthalmoscope that is intended for imaging and three-dimensional analysis of the fundus and retinal nerve fiber layer (RNFL) in vivo. The GDx and its GDx Variable Corneal Compensation (VCC) and GDx Enhanced Corneal Compensation (ECC) RNFL Normative Databases aid in the diagnosis and monitoring of diseases and disorders of the eye that may cause changes in the polarimetric retinal nerve fiber layer thickness. The GDx is to be used in patients who may have an optic neuropathy.

The GDxPRO is a confocal scanning laser ophthalmoscope comprising an optomechanical scanning laser head unit and a computer. The device employs Scanning Laser Polarimetry (SLP) to measure the Retinal Nerve Fiber Layer (RNFL) thickness using polarized light.

Here's a breakdown of the acceptance criteria and study information for the GDxPRO™ device based on the provided text:

1. Table of Acceptance Criteria and Reported Device Performance

The provided text for the GDxPRO™ does not explicitly state specific quantitative acceptance criteria or detailed device performance metrics that would typically be seen in a clinical study. Instead, the submission focuses on demonstrating substantial equivalence to a predicate device (GDx with ECC Retinal Nerve Fiber Layer Normative Database, K082016).

The key "performance" claimed is that the GDxPRO™ retains the functionality and safety/effectiveness of the predicate device.

2. Sample Size Used for the Test Set and Data Provenance

The provided text does not explicitly mention a separate "test set" sample size or data provenance (e.g., country of origin, retrospective/prospective). Since the submission focuses on substantial equivalence based on modifications to a predicate, it's possible that direct clinical efficacy testing on a dedicated test set, as might be done for a novel device, was not the primary focus here. The "evaluation performed on the GDxPRO" likely involved engineering and performance testing rather than a large-scale clinical study with a distinct patient test set.

3. Number of Experts Used to Establish Ground Truth for the Test Set and Qualifications

The provided text does not specify the number of experts or their qualifications for establishing ground truth for a test set. Given the focus on substantial equivalence based on modifications to an existing device, it's unlikely that a new, independent "ground truth" establishment process by external experts for a clinical dataset was undertaken as part of this 510(k) summary.

4. Adjudication Method for the Test Set

The provided text does not mention any adjudication method (e.g., 2+1, 3+1, none) for a test set. This further supports the interpretation that a traditional clinical study with independent review of cases by multiple experts was not the primary basis of this submission.

5. Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study

The provided text does not indicate that a Multi-Reader Multi-Case (MRMC) comparative effectiveness study was performed. Therefore, there is no information on the effect size of how much human readers improve with AI vs. without AI assistance. The GDxPRO™ as described here is an imaging device, not an AI diagnostic assistant, so an MRMC study in that context would not be relevant.

6. Standalone (Algorithm Only Without Human-in-the-Loop Performance) Study

The provided text does not describe a standalone (algorithm only) performance study. The GDxPRO™ is a medical imaging device used by clinicians. Its "performance" is tied to its ability to accurately measure RNFL thickness, which then aids in diagnosis and monitoring by a human ophthalmologist.

7. Type of Ground Truth Used

The provided text does not explicitly state the type of ground truth used in the context of clinical validation (e.g., expert consensus, pathology, outcomes data). However, since the device measures Retinal Nerve Fiber Layer (RNFL) thickness for aiding in diagnosis of optic neuropathy, the implicit "ground truth" for the predicate device (and thus the GDxPRO™'s equivalence) would ultimately relate to clinical diagnosis of optic neuropathy by healthcare professionals, potentially based on a combination of clinical findings, imaging, and long-term outcomes for the normative databases.

8. Sample Size for the Training Set

The provided text does not specify a sample size for a "training set." The GDxPRO™ applies a "Normative Database" (GDx Variable Corneal Compensation (VCC) and GDx Enhanced Corneal Compensation (ECC) RNFL Normative Databases). These databases would have been built from a large population of healthy and diseased eyes. While the exact sample size for the original database development is not in this document, it would likely be significant to establish normative ranges.

9. How the Ground Truth for the Training Set Was Established

The text mentions "GDx Variable Corneal Compensation (VCC) and GDx Enhanced Corneal Compensation (ECC) RNFL Normative Databases." These databases are fundamental to the device's function, as they provide a reference against which patient RNFL measurements are compared.

The ground truth for these normative databases would have been established by:

• Clinical examinations: Identifying healthy individuals to form the "normal" range.
• Clinical diagnosis: Identifying individuals with various "diseases and disorders of the eye that may cause changes in the polarimetric retinal nerve fiber layer thickness" (e.g., optic neuropathy, glaucoma) to understand abnormal ranges.
• Longitudinal follow-up: In some cases, tracking disease progression or stability to correlate RNFL changes with clinical outcomes.

This process would involve expert ophthalmologists classifying patients and their conditions, likely through a combination of clinical assessment tools, other imaging modalities, and potentially histopathology in cases where it's relevant and obtainable (though less common for RNFL thickness in living patients). The "ground truth" for a normative database essentially means accurate classification of individuals as healthy or having a specific condition based on established clinical criteria at the time the database was built.

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