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510(k) Data Aggregation
(107 days)
CENTERVUE DIGITAL RETINOGRAPHY SYSTEM
The CenterVue Digital Retinography System DRS is intended for taking digital images of a human retina without the use of a mydriatic agent.
The DRS is an ophthalmic camera used to take colored digital images of a human retina without use of a mydriatic agent. The instrument body includes: optical head, head-rest, chin rest and a base. The device integrates a dedicated computer and a touch-screen display and operates as a standalone unit, running a dedicated software application. The device is provided with a front lens cap, a power cord and spare fuses.
The provided document describes the CenterVue Digital Retinography System (DRS), an ophthalmic camera. The performance testing section outlines its acceptance criteria and the results of a study demonstrating its performance.
1. Table of Acceptance Criteria and Reported Device Performance
| Performance Item | Acceptance Criteria | Reported Device Performance |
|---|---|---|
| Sensor resolution | 48 pixels / degree | 48.15 pixels / degree |
| Resolution on retina | ≥ 60 line pairs/mm at the center of the field | |
| ≥ 40 line pairs/mm at the mid field (r/2) | ||
| ≥ 25 line pairs/mm at the periphery of the field (r) | Yes | |
| Yes | ||
| Yes | ||
| Field of view | 45° horizontal x 40° vertical | 42.95° horizontal x |
| 40.3° vertical | ||
| Pixel pitch | 6 μm | 6.39 μm |
| Range of focus | -15 D to + 15 D | -15 D to + 15 D |
| Minimum pupil size | 4.0 mm | 4.0 mm |
| Position of internal | ||
| fixation targets for | ||
| central and peripheral | ||
| fields | CENTRAL: field centered on the foveal pit | |
| NASAL: field centered 17° nasally to the fovea | ||
| TEMPORAL: field centered 17° temporally to the fovea | ||
| SUPERO-TEMPORAL: field centered 12° superiorly and 12° | ||
| temporally to the fovea | ||
| PERI-CENTRAL: field centered 5° nasally to the fovea | ||
| SUPERIOR: field centered 17° superiorly to the fovea | ||
| INFERIOR: field centered 17° inferiorly to the fovea | Actual position (for | |
| normally fixating | ||
| subjects) within ±1° | ||
| from expected | ||
| position | ||
| OD/OS detection | Device captures OD or OS or both based on operator's | |
| selection | Yes (tested on | |
| multiple subjects) | ||
| Auto-alignment | Device automatically aligns to the center of the pupil prior | |
| to auto-focusing and capture | Yes (tested on | |
| multiple subjects) |
2. Sample Size Used for the Test Set and Data Provenance
The document does not specify a precise sample size for the test set in terms of number of patients or images. For several performance items ("OD/OS detection" and "Auto-alignment"), it states "tested on multiple subjects" without providing a specific count.
The data provenance is not explicitly stated in terms of country of origin or whether it was retrospective or prospective.
3. Number of Experts Used to Establish the Ground Truth for the Test Set and Their Qualifications
The document does not describe the use of human experts or ground truth establishment by experts for the performance testing cited. The performance items are technical specifications of the device.
4. Adjudication Method for the Test Set
Not applicable, as the performance testing focuses on technical specifications of the device rather than diagnostic or interpretative outcomes that would require adjudication.
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
An MRMC comparative effectiveness study is not mentioned in the document. The DRS is an imaging device, not an AI-assisted diagnostic tool.
6. If a Standalone (i.e. algorithm only without human-in-the loop performance) was done
The performance tests described are for the standalone device's technical specifications and functionality, as it is an imaging system. It assesses the device's ability to capture images with specific technical characteristics.
7. The Type of Ground Truth Used
The "ground truth" for the performance testing appears to be based on physical measurements, calibrations, and functional checks against engineering specifications, rather than clinical outcomes, pathology, or expert consensus on medical conditions. For example, resolution is measured in line pairs/mm or pixels/degree, and angles in degrees.
8. The Sample Size for the Training Set
A training set is not mentioned, as this document describes performance testing for a medical imaging device's technical specifications, not a machine learning or AI model.
9. How the Ground Truth for the Training Set was Established
Not applicable, as there is no mention of a training set or ground truth in the context of machine learning.
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