K121739 · Optovue, Inc. · HLI · Jan 18, 2013 · Ophthalmic
Device Facts
Record ID
K121739
Device Name
IVUE WITH NORMATIVE DATABASE
Applicant
Optovue, Inc.
Product Code
HLI · Ophthalmic
Decision Date
Jan 18, 2013
Decision
SESE
Submission Type
Traditional
Regulation
21 CFR 886.1570
Device Class
Class 2
Attributes
Real-World Evidence
Real-World Evidence
Submission
Device
Sponsor
RWD Sources
RWE Use Summary
Key Tags
K121739 · Jan 18, 2013
IVUE WITH NORMATIVE DATABASE
Optovue, Inc.
Normative database of known normal subjects
The normative database is used as a quantitative reference tool to compare patient measurements (retina, retinal nerve fiber layer, ganglion cell complex, and optic disc) against a distribution of normal subjects to aid in the diagnosis and management of ocular health and diseases.
iVue Normative Database Collection; Retrospective/Observational data collection for normative reference
Normal subjects (adult population); Sample Size: Not explicitly stated (total number of normal subjects described as similar to predicate RTVue NDB)
Not applicable for this study
Normative limits for retinal, RNFL, and optic disc measurements
AI Performance
Output
Algorithm
Acceptance
Observed
Dev DS
Dev Readers
Test DS
Test Readers
Retina thickness measurements
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Repeatability and reproducibility study: 14 normal subjects, 13 patients with glaucoma, and 13 patients with retina disease.
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Ganglion cell complex (GCC) measurements
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Repeatability and reproducibility study: 14 normal subjects, 13 patients with glaucoma, and 13 patients with retina disease.
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Optic disc and retinal nerve fiber layer (RNFL) measurements
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Repeatability and reproducibility study: 14 normal subjects, 13 patients with glaucoma, and 13 patients with retina disease.
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Indications for Use
The iVue with Normative Database is an optical coherence tomography system intended for in vivo imaging, axial cross-sectional, three-dimensional imaging and measurement of anterior and posterior ocular structures.
Device Story
iVue with Normative Database (NDB) is a non-contact, high-resolution optical coherence tomography (OCT) system; uses low-coherence interferometry to measure reflectivity of retinal and corneal tissue. Input: ocular structure scans (Retina Map, Nerve Fiber, iWellness, 3-D disc). Processing: compares patient measurements to a normative database of known normal subjects; uses regression models to estimate normative limits; provides color-coded percentile categories (within normal, borderline, outside normal). Output: cross-sectional B-scans, 3D images, and quantitative analysis reports. Used in clinical settings; operated by eye care professionals. Healthcare providers use output as a clinical reference to aid in diagnosis, documentation, and management of ocular health and diseases, including glaucoma and retinal pathologies. Benefits include objective, quantitative assessment of ocular structures compared to normative distributions.
Clinical Evidence
Bench testing only. Precision study (repeatability and reproducibility) conducted with 14 normal, 13 glaucoma, and 13 retina disease subjects. Evaluated 4 scan patterns (ONH, Retina, GCC, iWellness) across 3 instruments/operators. Results provided as SD, COV, and 95% limits of reproducibility. Comparison study against RTVue predicate (K101505) demonstrated similar measurement means and agreement across all parameters.
Technological Characteristics
Non-contact OCT system; low-coherence interferometer; AC powered. No hardware changes from K091404. Software-based normative database comparison. Connectivity via standard PC interface. Biocompatibility and electrical safety (IEC-60601) unchanged from predicate.
Indications for Use
Indicated for adult population to aid in diagnosis, documentation, and management of ocular health and diseases. Provides non-contact, high-resolution tomographic imaging and measurement of anterior and posterior ocular structures (retinal nerve fiber layer, ganglion cell complex, optic disc, cornea, anterior chamber). Includes quantitative comparison of retinal nerve fiber layer, ganglion cell complex, and optic disc measurements against a normative database.
Regulatory Classification
Identification
An ophthalmoscope is an AC-powered or battery-powered device containing illumination and viewing optics intended to examine the media (cornea, aqueous, lens, and vitreous) and the retina of the eye.
Special Controls
*Classification.* Class II (special controls). The device, when it is an AC-powered opthalmoscope, a battery-powered opthalmoscope, or a hand-held ophthalmoscope replacement battery, is exempt from the premarket notification procedures in subpart E of part 807 of this chapter subject to the limitations in § 886.9.
Two short videos show you everything — or skip straight to the written tutorial if you'd rather read. You can reopen this any time from the Tutorial button in the top bar.
Part 1 — Search, results, and everyday workflows 16 min
Part 2 — Embeddings: the galaxy map 3 min
1. Search: exact and fuzzy
Type a phrase like "coronary artery calcification" into the search box. You get two kinds of results. Exact results match the literal phrase — prefix searches work ("coronary artery calcificati") but suffix searches do not. Fuzzy results match on the meaning and intent of your phrase rather than the exact words, and are sorted by relevance score. Hover over the Exact or Fuzzy badge on any row to see exactly why it matched.
Use the checkboxes above the results to narrow: SaMD keeps only software-only devices, AI / ML keeps only devices with AI.
Exact vs. fuzzy search: what's the difference?
Exact matches on the literal phrase (prefix search works, suffix does not). Fuzzy matches on the meaning and intent of the phrase rather than the exact words. Hover over the badge on any row to see why it matched.
You search "coronary artery calcification" and want only software devices with AI. What two filters do you apply?
Narrow by SaMD (software-only devices), then narrow by AI/ML (devices with AI).
2. The results table
Scroll right in the results table. The intended use is extracted for you — no need to open the PDF. The device story gives a high-level snapshot of what the device does and how it's used. The AI Performance sub-table shows each output name, acceptance criteria, observed values, and development/test dataset descriptions — the same format Innolitics uses for regulatory strategy outputs, and the fastest high-level fingerprint of an AI device. It is AI-generated but has been very reliable in practice.
Where do you find a device's intended use without opening the PDF?
Scroll right in the search results table. The intended use column is extracted for you; no need to dig into the 510(k) summary PDF.
What does the AI Performance sub-table show, and why is it useful?
Output name, acceptance criteria, observed values, development dataset description, and test dataset description. It's the same format we use for regulatory strategy output and Fast 510(k) input, and the fastest high-level fingerprint of an AI device. AI-generated but reliable in practice.
3. Judging fuzzy relevance
Fuzzy results trail off in relevance as you scroll. Use three signals to decide how far down to go: the fuzzy badge explanations, the intended use column, and whether your target output (e.g., Cobb angle) still appears in the AI Performance sub-table. Once it stops appearing, you're past the relevant zone. A top hit with a low score (~0.4) and a stretched explanation is a hint the closest predicates are far away — the project may be headed for De Novo. Note the fuzzy search is a pattern match: it doesn't handle negation ("not") well, and hardware devices can appear — filter by SaMD/AI ML to cut them.
How do you judge how far down fuzzy search results to go?
Use the relevancy signals: the fuzzy badge explanations, the intended use column, and whether the target output (e.g., Cobb angle) still appears in the AI Performance sub-table. Once it stops appearing, results are trailing off in relevancy.
4. Device detail page: chat and citations
Click a device name to open its detail page: device facts on the left, a chat window on the right. Ask something like "Describe the training data". The answer carries little citation bubbles — click one to jump to the highlighted passage in the source PDF, so you can verify every AI answer against the document. There's also a Download PDF button for sharing.
How do you verify an AI chat answer on the device detail page?
Click the citation bubbles to jump to the relevant highlight in the source document.
Reading rule for every project: how many summaries do you read in full?
At least the three most relevant 510(k) or De Novo summaries, in full. After that, use targeted chat questions to confirm your memory quickly. The tool supports this professional habit — it doesn't replace it.
5. Side-by-side comparison
Select multiple rows in the results table (aim for under ~10), then open the PDF Viewer tab. Ask one question — it goes to all selected devices in parallel, each with citations. This is the fastest way to compare and contrast devices: training data, PCCP scope, how they handled adding new scanners, and so on.
What does the side-by-side PDF viewer mode do?
Select multiple devices, open the PDF viewer tab, and ask one question (e.g., "Describe the training data"). It queries all selected devices simultaneously with citations, so you can compare and contrast quickly.
6. Collections
With rows selected, go to the Collections tab and create a labeled collection (e.g., "Cobb Angle Project"). Reload that selection any time — before a client call, pull up the collection and ask questions across all of its devices at once.
How do you save a set of selected devices for later use?
Select the rows, go to the Collections tab, and create a labeled collection (e.g., "Cobb Angle Project"). You can reload the selection anytime and carry it into the PDF viewer and other tabs that support selections.
7. Product codes and the regulations tree
Click a product code in the results to jump to it in the regulations tree — identification text, sibling product codes, and devices you can open in a PDF viewer on the right. Click a regulation number to see its identification, special controls, and related product codes. You can also search by product code or regulation number at the top of the tree. Always read the special controls if any exist for your device — it broadens your search and sharpens pre-kickoff research.
What can you do from the regulations tree view?
Browse product codes and regulation numbers, read the identification text and special controls, browse sibling product codes, open device PDFs on the right, and search by product code or regulation number at the top of the tree.
8. Chart view
Click Show Chart and segment by regulation number (or product code) to see which regulations dominate your result set. Clicking a regulation takes you into the regulations tree. Great for spotting that most matches are, say, hardware laparoscopic devices — a cue to go back and filter.
How do you see which regulations dominate a search result set?
Click "Show Chart" and segment by Regulation Number. Clicking a regulation takes you to the regulations tree.
9. The predicate graph
Open the Predicates tab for a family-tree view of predicate relationships. Click a node to trace its parents and children; selections from search carry over pre-selected. Commonly predicated devices are worth reading — a lot of people predicated them for a reason. The visual lineage is also handy on client calls, e.g. to show how a predicate family evolved and justify why your predicate still holds.
In the predicate graph, why are commonly predicated devices worth reading?
A lot of people predicated them for a reason. Clicking a node traces parents and children, and selections from search carry over pre-selected.
10. Embeddings: the galaxy map
The Embeddings tab plots every matching document in a 2-D "galaxy map" where semantically similar devices cluster together. Hover or click clusters to explore, and let AI label the clusters for you. Embeddings beat product codes for grouping: two devices can carry different product codes (LLZ vs. QIH) yet do the same thing — the embedding captures the meaning of the intended use and device story. This is also exactly how retrieval-augmented generation (RAG) works under the hood, and it makes a great visual on client calls.
Try it yourself
Head to the search page and work through a few of these AI/ML fuzzy searches to build intuition: perivascular fat on CT · aortic valve calcification opportunistic screening on noncontrast CT · breast cancer prediction on digital pathology slides · autism detection · gestational age prediction · a hearing aid that can also detect a pulse · foundation model based analysis of ECG · large language models · penetration test. Watch how the relevance scores, intended use, and AI Performance tables tell you when results stop being meaningful.