CHEMVIEW-10™ is a visual qualitative and semi-quantitative test for the determination of urobilinogen, glucose, ketones, bilirubin, protein, nitrite, pH, blood, specific gravity and leukocytes in urine. These metabolites are useful in the evaluation of renal, urinary and metabolic disorder. The product will be marketed to physicians' office laboratories, clinics, hospitals and reference laboratories.
Device Story
CHEMVIEW-10™ is a reagent test strip for urinalysis; provides visual qualitative and semi-quantitative results for ten analytes: urobilinogen, glucose, ketones, bilirubin, protein, nitrite, pH, blood, specific gravity, and leukocytes. Used in clinical settings (physicians' offices, clinics, hospitals, reference labs) by healthcare professionals. Principle of operation involves colorimetric chemical reaction on test pads; user compares strip color changes against a reference chart to interpret results. Output assists clinicians in evaluating renal, urinary, and metabolic disorders. Benefits include rapid, point-of-care diagnostic screening for common urinary metabolites.
Clinical Evidence
Bench testing only. Comparison study conducted at three reference laboratories using 188 fresh and spiked urine specimens. Compared CHEMVIEW-10™ against Roche Chemstrip®. Glucose results showed 99.5% agreement within the same color block and 100% within one color block. Occult blood results showed 86.7% agreement within the same color block and 97.34% within one color block. No clinical patient outcome data provided.
Technological Characteristics
Solid-phase reagent test strip; 10 reagent areas per strip. Qualitative colorimetric enzymatic reaction principle. No energy source; visually read. Packaged in desiccated plastic vial. Shelf life: 2 years unopened, 6 months opened. No software or electronic components.
Indications for Use
Indicated for use in physicians' office laboratories, clinics, hospitals, and reference laboratories for the visual qualitative and semi-quantitative determination of urobilinogen, glucose, ketones, bilirubin, protein, nitrite, pH, blood, specific gravity, and leukocytes in human urine.
Regulatory Classification
Identification
A urinary glucose (nonquantitative) test system is a device intended to measure glucosuria (glucose in urine). Urinary glucose (nonquantitative) measurements are used in the diagnosis and treatment of carbohydrate metabolism disorders including diabetes mellitus, hypoglycemia, and hyperglycemia.
Predicate Devices
Roche Chemstrip®
Submission Summary (Full Text)
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# 510(k) SUBSTANTIAL EQUIVALENCE DETERMINATION DECISION SUMMARY ASSAY ONLY TEMPLATE
A. 510(k) Number:
k042641
B. Purpose for Submission:
New test strip for urinalysis
C. Measurand:
Glucose
Blood
Note: other analytes on the test strip (urobilinogen, ketones, bilirubin, protein, nitrite, pH, specific gravity, leukocytes) are Class I exempt and therefore not reviewed.
D. Type of Test:
Qualitative colorimetric test
E. Applicant:
Cenogenics Corporation
F. Proprietary and Established Names:
CHEMVIEW-10
G. Regulatory Information:
1. Regulation section:
21 CFR §862.1340 Urinary glucose (nonquantitative) test system
21 CFR §864.6550 Reagent, Occult Blood
2. Classification:
Class II (glucose and blood)
3. Product code:
JIL
KHE
4. Panel:
Chemistry (75)
Hematology (81)
H. Intended Use:
1. Intended use(s):
See below.
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2. Indication(s) for use:
“CHEMVIEW-10 is a visual qualitative and semi-quantitative test for the determination of urobilinogen, glucose, ketones, bilirubin, protein, nitrite, pH, blood, specific gravity, and leukocytes in urine.
The product will be marketed to physicians’ office laboratories, clinics, hospitals, and reference laboratories.”
3. Special conditions for use statement(s):
For prescription use only.
4. Special instrument requirements:
None required; this is a single-use visually read device.
I. Device Description:
The device is a reagent test strip for urinalysis. It consists of 100 individual strips packed in a desiccated plastic vial with a full-color chart for reading test results on the label, and product instructions. Each individual strip has 10 different solid-phase reagent areas.
J. Substantial Equivalence Information:
1. Predicate device name(s):
Roche Chemstrip 10 with SG
2. Predicate 510(k) number(s):
k896454
3. Comparison with predicate:
Both devices are visual qualitative tests, measure the same analytes, have the same or very similar detection ranges, and use the same test principles. The tests have different manufacturers.
K. Standard/Guidance Document Referenced (if applicable):
None referenced by the submission.
L. Test Principle:
Solid-phase qualitative colorimetric enzymatic reactions are used by each test.
Glucose: Glucose oxidase catalyzes the formation of gluconic acid and hydrogen peroxide from the oxidation of glucose. Peroxidase then catalyzes the reaction of hydrogen peroxide with potassium iodide chromogen to form colors ranging from blue to dark brown depending on the glucose concentration.
Blood: The psuedoperoxidase activity of hemoglobin catalyzes the reaction of o-tolidine and cumene hydroperoxide to form colors ranging from sky blue to dark blue depending in the amount of blood in the urine.
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M. Performance Characteristics (if/when applicable):
1. Analytical performance:
Only glucose and occult blood, as Class II analytes, are subject to performance characteristic review.
a. Precision/Reproducibility:
**Glucose:** Reproducibility of glucose results was tested with standard solutions of glucose prepared in urine negative for glucose. Each solution was tested with 10 CHEMVIEW strips from three different lots:
Glucose Reproducibility Testing with the CHEMVIEW-10
| | Glucose Concentration | | | | |
| --- | --- | --- | --- | --- | --- |
| | 0 mg/dL | 100 mg/dL | 250 mg/dL | 500 mg/dL | 1000 mg/dL |
| Expected Result | - | +/- | + | ++ | +++ |
| Lot 1 | 10/10* | 10/10 | 10/10 | 10/10 | 10/10 |
| Lot 2 | 10/10 | 10/10 | 10/10 | 10/10 | 10/10 |
| Lot 3 | 10/10 | 10/10 | 10/10 | 10/10 | 10/10 |
* Number of strips with expected results
**Occult Blood:** Reproducibility of occult blood results was tested with standard solutions of glucose prepared in urine negative for glucose. Each solution was tested with 10 CHEMVIEW strips from three different lots:
Occult Blood Reproducibility Testing with the CHEMVIEW-10
| | Hemoglobin Concentration | | | | | |
| --- | --- | --- | --- | --- | --- | --- |
| | 0 mg/dL | 0.015 mg/dL | 0.03 mg/dL | 0.06 mg/dL | 0.15 mg/dL | 0.75 mg/dL |
| Expected Result | - | +/- | +/- | + | ++ | +++ |
| Lot 1 | 10/10* | 10/10 | 10/10 | 10/10 | 10/10 | 10/10 |
| Lot 2 | 10/10 | 10/10 | 10/10 | 10/10 | 10/10 | 10/10 |
| Lot 3 | 10/10 | 10/10 | 10/10 | 10/10 | 10/10 | 10/10 |
* Number of strips with expected results
b. Linearity/assay reportable range:
Not applicable.
c. Traceability, Stability, Expected values (controls, calibrators, or methods):
No traceability was provided.
Unopened shelf life of the strips is 2 years from the date of manufacture; opened, the strips are stable for 6 months. Real-time studies established these parameters.
Calibration of the CHEMVIEW-10 by the user is not required.
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d. Detection limit:
**Glucose:**
Standard solutions of glucose were prepared in urine negative for glucose at concentrations of 100 mg/dL, 250 mg/dL, 500 mg/dL, and 1000 mg/dL. Each solution and negative urine was tested with ten strips. The expected results were obtained 10/10 times at all concentrations tested, demonstrating a glucose sensitivity of 100 mg/dL.
**Occult Blood:**
Standard solutions of hemoglobin were prepared in urine negative for occult blood at concentrations of 0.015 mg/dL, 0.03 mg/dL, 0.06 mg/dL, 0.15 mg/dL, and 0.75 mg/dL. Each solution and negative urine was tested with ten strips. The expected results were obtained 10/10 times at all concentrations tested, demonstrating occult blood sensitivity of 0.015 mg/dL.
Red blood cell suspensions were prepared in normal urine at concentrations of 5 cells/ul, 10 cells/ul, 20 cells/ul, 50 cells/ul, and 250 cells/ul. Cell count was confirmed by microscopic examination. Each suspension and a negative urine sample were tested with ten strips. Expected results (10/10 samples) were obtained at all concentrations except 5 cells/ul, were 3/10 samples were positive. Thus, the data demonstrates that the CHEMVIEW-10 occult blood test can detect 10 intact red blood cells per microliter of urine.
e. Analytical specificity:
**Glucose:** Specificity of the test for glucose was challenged by testing the strip with solutions of lactose, fructose, and galactose prepared in normal urine. Ten strips were tested at five concentrations up to 1000 mg/dL; no positive results were observed.
Interfering substances were tested by preparing glucose solutions in normal urine then adding different concentrations of ascorbic acid, acetoacetic acid (for ketone bodies). Each combination was tested with 10 CHEMVIEW-10 test strips. Ascorbic acid concentrations of 60 mg/dL or greater, and ketone bodies in concentrations greater than 80 mg/dL may cause false negatives in urine samples containing a small amount of glucose. At high pH (pH 9) the color reaction on the strip was slower to develop and the reactivity was decreased. Specific gravity, tested between 1.005 and 1.030, did not affect the glucose test.
**Occult Blood:** Solutions of myoglobin were prepared in normal urine at six concentrations up to 0.75 mg/dL. Each solution was tested ten times; the expected result was seen in all tests, indicating that the CHEMVIEW-10 occult blood test is as sensitive to myoglobin as to hemoglobin.
Substances that were tested for interference with the occult blood test were:
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bacteria, ascorbic acid, protein, hypochlorites, and specific gravity. Ten strips were used at each test point; standard hemoglobin solutions were prepared in normal urine. Nitrite-positive bacterially contaminated urine specimens may produce a false positive. Increasing concentrations of ascorbic acid decreases the reactivity of the occult blood test, particularly at lower hemoglobin concentrations. Increasing concentrations of protein reduced the reactivity of the occult blood test. Testing hypoclorites showed that even trace amounts of strong oxidizing agents may produce a false positive occult blood test result. Sensitivity of the occult blood test may be reduced in specimens with low specific gravity.
f. Assay cut-off:
Not applicable.
2. Comparison studies:
a. Method comparison with predicate device:
Clinical urine samples (n=142) were compared to the predicate at three different clinical laboratories. An additional 23 spiked samples were tested at two of these laboratories (n=46) for a total of 188 samples. The methods described in the package inserts were followed.
Glucose:
Agreement between the two strips was 99.5% within the same color block and 100% within one color block.
Occult Blood:
Agreement between the tests was 86.7% within the same color block and 97.3% within one color block. Interpretation of the agreement between the tests is challenging because the scale of the two tests does not exactly correspond. Only three of 188 samples were markedly discrepant. The other discrepant results varied by one color block, usually as a difference between 'trace' and negative
b. Matrix comparison:
Not applicable; this device is only used with urine.
3. Clinical studies:
a. Clinical Sensitivity:
Not applicable.
b. Clinical specificity:
Not applicable.
c. Other clinical supportive data (when a. and b. are not applicable):
Not applicable.
4. Clinical cut-off:
Not applicable.
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5. Expected values/Reference range:
The following should not be detectable in the urine of healthy persons with this test: glucose, blood, ketones, bilirubin, nitrite, and leukocytes.
Protein: Normal urine may contain a small amount of protein; consistently elevated levels of protein indicate further clinical testing.
pH: Normal urine is usually slightly acidic, with a pH around 6.
Specific gravity: Normal specific gravity in adults ranges from 1.002 – 1.030 depending on fluid intake and state of hydration.
N. Proposed Labeling:
The labeling is sufficient and it satisfies the requirements of 21 CFR Part 809.10.
O. Conclusion:
The submitted information in this premarket notification is complete and supports a substantial equivalence decision.
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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.