COBAS INTEGRA Ammonia (NH3): contains an in vitro diagnostic reagent system intended for use on COBAS INTEGRA for the quantitative determination of the ammonia concentration in plasma (test NH3, 0-045). COBAS INTEGRA aAmylase EPS (AMYLL): contains an in vitro diagnostic reagent system intended for use on COBAS INTEGRA for the quantitative determination of the catalytic activity of amylase in serum, plasma (test AMY-L, 0998) and urine (test AMY-UL 0-999). COBAS INTEGRA Cholesterol (CHOLL): contains an in vitro diagnostic reagent system intended for use on COBAS INTEGRA for the quantitative determination of total cholesterol (test CHOLL, 0-001) and HDL cholesterol concentration in serum and plasma in clinical laboratories. COBAS INTEGRA HDL Cholesterol Application (HDLL): contains an in vitro diagnostic reagent system intended for use on COBAS INTEGRA for the quantitative determination of total cholesterol and HDL - cholesterol (test HDLL, 0-002) concentration in serum and plasma in clinical laboratories. COBAS INTEGRA Creatinine Enzymatic (CREAE): contains an in vitro diagnostic reagent system intended for use on COBAS INTEGRA for the quantitative determination of the creatinine concentration in serum (test CREAE, 0-014), and urine (test CREEU, 0-114). COBAS INTEGRA Digitoxin (DIGIT): contains an in vitro diagnostic reagent system intended for use on COBAS INTEGRA for the quantitative determination of digitoxin in serum or heparinized plasma (test DIGIT 0-259). COBAS INTEGRA Gamma Glutamyltransferase (GGTL): contains an in vitro diagnostic reagent system intended for use on COBAS INTEGRA for the quantitative determination of the catalytic activity of GGT, (EC 2.3.2.2; y-glutamyl peptide: amino acid y-glutamyltransferase) in serum and plasma (test GGTL, 0-599). COBAS INTEGRA Glucose HK Liquid (GLUCL): contains an in vitro diagnostic reagent system intended for use on COBAS INTEGRA for the quantitative determination of the glucose concentration in serum, plasma (test GLUL, 0-991), urine (test GLULU, 0-992), and cerebrospinal fluid (test GLULC, 0-993). COBAS INTEGRA Lipase (LIPL): contains an in vitro diagnostic reagent system intended for use on COBAS INTEGRA for the quantitative determination of the catalytic activity of lipase in serum and plasma (test LIPL, 0-200). COBAS INTEGRA Lysergic acid diethylamide (LSD): contains an in vitro diagnostic reagent system intended for use on COBAS INTEGRA for the qualitative determination of lysergic acid diethylamide (LSD) in urine (test LSD, 0-001). COBAS INTEGRA Urea/BUN (UREAL): contains an in vitro diagnostic reagent system intended for use on COBAS INTEGRA for the quantitative determination of the urea/BUN (blood urea nitrogen), in serum, plasma (test UREL, 0-003) and urine (test URELU, 0-004). Roche TDM OnLine Digitoxin Calibrators: are intended for use with the Roche reagents for Digitoxin and the COBAS Chemistry systems for the quantitative determination of digitoxin in serum and plasma. Roche TDM OnLine Digitoxin Controls: are quality control samples intended for use on COBAS chemistry systems with Roche reagents and calibrators for the quantitative determination of digitoxin assays.
Device Story
COBAS INTEGRA system uses reagent cassettes for automated in vitro diagnostic testing; analyzer performs chemistry, drugs of abuse, immunology, and TDM assays. System utilizes absorbance, fluorescence polarization, and ion-selective electrodes; throughput up to 600 tests/hour. Reagent cassettes are barcode-identified, preparation-free, and stored on-board at 2-8°C. Input samples (serum, plasma, urine, CSF) are processed via robotics; analyzer provides quantitative or qualitative results to clinicians. System streamlines workflow via random access and STAT prioritization. Benefits include improved time management, reduced manual preparation, and consistent diagnostic accuracy for clinical decision-making.
Clinical Evidence
Bench testing only. Performance evaluated via precision (within-run and total CV%), sensitivity, and accuracy (correlation with predicate methods). Sample sizes ranged from 114 to 256. Correlation coefficients (r) were consistently high (0.973–0.999). Results confirm performance is equivalent to legally marketed predicate devices.
Technological Characteristics
Automated analyzer using absorbance, fluorescence polarization, and ion-selective electrodes. Reagents are liquid-based (previously granulate). Methodology includes enzymatic colorimetric, kinetic interaction of microparticles in solution (KIMS), and turbidimetric methods. System features barcode readers for reagent/sample identification, on-board refrigerated storage (2-8°C), and robotic sample handling. Connectivity includes automated data reading from cassette labels.
Indications for Use
Indicated for quantitative or qualitative in vitro diagnostic determination of analytes (Ammonia, Amylase, Cholesterol, HDL-Cholesterol, Creatinine, Digitoxin, GGT, Glucose, Lipase, LSD, Urea/BUN) in human serum, plasma, urine, or CSF. Used in clinical laboratory settings for patient monitoring and diagnostic testing.
Regulatory Classification
Identification
An amylase test system is a device intended to measure the activity of the enzyme amylase in serum and urine. Amylase measurements are used primarily for the diagnosis and treatment of pancreatitis (inflammation of the pancreas).
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.