ADVIA CHEMISTRY CALCIUM_2 (CA_2) METHOD, MODELS P/M 02189699 (40 ML FILL), P/N 02189915 (70 ML FILL)
Applicant
Siemens Healthcare Diagnostics
Product Code
CJY · Clinical Chemistry
Decision Date
Apr 2, 2009
Decision
SESE
Submission Type
Traditional
Regulation
21 CFR 862.1145
Device Class
Class 2
Indications for Use
For in vitro diagnostic use in the quantitative determination of calcium in human serum, plasma, and urine on the ADVIA Chemistry systems. Such measurements are used in the diagnosis and treatment of parathyroid disease, a variety of bone diseases, chronic renal failure, and tetany.
Device Story
ADVIA Chemistry Calcium_2 Method is a quantitative, colorimetric in vitro diagnostic assay for use on ADVIA Chemistry systems. Input: human serum, plasma, or urine samples. Principle: sample prediluted 5-fold with saline; calcium ions react with Arsenazo III reagent at pH 5.9 to form a purple complex; absorbance measured at 658 nm. Output: calcium concentration proportional to color intensity. Used in clinical laboratories by trained personnel. Results assist clinicians in diagnosing and managing parathyroid, bone, and renal diseases.
Clinical Evidence
Bench testing only. Precision evaluated per CLSI EP05-A2 (n=40 replicates); total CVs ranged 0.8-2.1% (serum) and 1.6-3.0% (urine). Linearity confirmed across 1-16 mg/dL (serum) and 1-32 mg/dL (urine). Method comparison with predicate (n=172 serum, n=50 urine) showed high correlation (r=0.996 serum, r=0.998 urine). Interference testing showed no significant interference for hemoglobin, bilirubin, triglycerides, or gadolinium contrast agents.
Technological Characteristics
Colorimetric assay; Arsenazo III reagent; single liquid reagent format; measures absorbance at 658/694 nm; automated on ADVIA Chemistry systems.
Indications for Use
Indicated for quantitative determination of calcium in human serum, plasma, and urine for patients requiring assessment of parathyroid disease, bone diseases, chronic renal failure, or tetany.
Regulatory Classification
Identification
A calcium test system is a device intended to measure the total calcium level in serum. Calcium measurements are used in the diagnosis and treatment of parathyroid disease, a variety of bone diseases, chronic renal disease and tetany (intermittent muscular contractions or spasms).
Predicate Devices
Calcium method for the Bayer ADVIA 1650 system (k991576)
Submission Summary (Full Text)
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# 510(k) SUBSTANTIAL EQUIVALENCE DETERMINATION DECISION SUMMARY ASSAY ONLY TEMPLATE
A. 510(k) Number:
k083386
B. Purpose for Submission:
New Device
C. Measurand:
Calcium
D. Type of Test:
Quantitative, colorimetric assay
E. Applicant:
Siemens Healthcare Diagnostics
F. Proprietary and Established Names:
ADVIA Chemistry Calcium_2 Method
G. Regulatory Information:
| Product Code | Classification | Regulation Section | Panel |
| --- | --- | --- | --- |
| CJY | Class II | 21 CFR§ 862.1145 | Clinical Chemistry (75) |
H. Intended Use:
1. Intended use(s):
See indications for use below.
2. Indication(s) for use:
For in vitro diagnostic use in the quantitative determination of calcium in human serum, plasma, and urine on the ADVIA Chemistry systems. Such measurements are used in the diagnosis and treatment of parathyroid disease, a variety of bone diseases, chronic renal failure, and tetany.
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3. Special conditions for use statement(s):
For prescription use only
4. Special instrument requirements:
ADVIA 1650 Chemistry system
I. Device Description:
The device is sold as two options: option 1 - 7x40mL wedges (38mL fill each) in a kit, option 2 - 8x70mL wedges (68mL fill each) in a kit. Reagents are provided ready to use. The reagent component concentrations are as follows: Sodium acetate, pH 5.9, 54.2 mmol/L, Arsenazo III 188 μmol/L, and non-reactive stabilizers.
J. Substantial Equivalence Information:
1. Predicate device name(s):
Calcium method for the Bayer ADVIA 1650 system (currently Siemens)
2. Predicate 510(k) number(s):
k991576
3. Comparison with predicate:
| Similarities | | |
| --- | --- | --- |
| Characteristics | Candidate Device: Siemens ADVIA Calcium_2 | Predicate Device: Bayer ADVIA 1650 Calcium |
| Intended Use | For in vitro diagnostic use in the quantitative determination of calcium in human serum, plasma, and urine on the ADVIA Chemistry systems. Such measurements are used in the diagnosis and treatment of parathyroid disease, a variety of bone diseases, chronic renal failure, and tetany. | same |
| Sample Type | Serum, plasma (Li-heparin) and urine | Serum, plasma (Li-heparin) and urine |
| Instrument | ADVIA® Chemistry 1650 system | ADVIA® Chemistry systems |
| Calibrators | Siemens Chemistry Calibrator (k030169) | Same (k030169) |
| Controls used | BioRad, or other commercial controls | BioRad, or other commercial controls |
| Method | colorimetry | colorimetry |
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| Differences | | |
| --- | --- | --- |
| Characteristics | Siemens ADVIA Calcium_2 | Bayer ADVIA 1650 Calcium |
| Assay Protocol | Calcium ions form a colored complex with Arsenazo III, which is measured at 658/694 nm. The amount of calcium present in the sample is directly proportional to the intensity of the colored complex formed.
**Reaction Equation**
$$Ca^{2+} + \text{Arsenazo III} \rightarrow \text{Ca-} \text{Arsenazo III Complex (purple)}$$ | Calcium ions form a violet complex with o-cresolphthalein complexone in an alkaline medium. The reaction is measured at 545/658 nm.
**Reaction Equation**
$$CPC + 2 Ca^{2+} \rightarrow CPC (Ca^{2+})_{2}$$
Complex |
| Reagents | One liquid reagent | Two liquid reagents |
| Measuring Range * | **Serum/Plasma:**
1.0 – 16.0 mg/dL (0.25 – 4.0 mmol/L)
**Urine:**
1.0 – 32.0 mg/dL (0.25 – 8.0 mmol/L) | **Serum/ Plasma:**
1.0 – 15.0 mg/dL (0.25 – 3.75 mmol/L)
**Urine:**
1.0 – 30.0 mg/dL (0.25 – 7.50 mmol/L) |
| Interfering Substances ** | Bilirubin–NSI to 50 mg/dL
Hemoglobin–NSI up to 1000 mg/dL
Lipemia (Intralipid)–NSI to 1000 mg/dL | Bilirubin–NSI to 30 mg/dL
Hemoglobin–NSI up to 525 mg/dL
Lipemia (Intralipid)–NSI to 650 mg/dL |
| Precision * | 2.1% at 5.8 mg/dL (serum)
1.3% at 9.8 mg/dL (serum)
0.8% at 13.8 mg/dL (serum)
3.0% at 6.0 mg/dL Ca (urine)
2.1% at 23.6 mg/dL Ca (urine) | 2.7% at 5.9 mg/dL (serum)
2.9% at 10.8 mg/dL (serum)
3.5% at 12.0 mg/dL (serum)
2.4% at 6.2 mg/dL (urine)
2.5% at 21.2 mg/dL Ca (urine) |
| Accuracy / Correlation * | **Serum :**
$$y = 0.98 x + 0.44; S_{y,x} = 0.21;$$
$$r = 0.993$$ (vs. ADVIA 1650 Ca) | **Serum :**
$$y = 0.99 x + 0.13; S_{y,x} = 0.22; r = 0.971$$ (vs. Technicon DAX) |
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| | Urine : y = 0.97 x + 0.30; Sy,x=0.34 ; r= 0.998 (vs. ADVIA 1650 Ca) | Urine : y = 1.07 x + 0.03; Sy,x = 0.56 ; r = 0.988 (vs. Beckman CX3) |
| --- | --- | --- |
| Traceability | Traceable to Inductively Coupled Plasma Atomic Emission, which uses reference materials from the NIST *** | Traceable to the NIST*** atomic absorption reference method |
* - data for both devices shown from ADVIA 1650/1800 performance
** - NSI – No Significant Interference
*** - National Institute of Standards and Technology
## K. Standard/Guidance Document Referenced (if applicable):
- CLSI EP17-A – Protocols for Determination of Limits of Detection and Limits of Quantitation; Approved Guideline
- CLSI EP05-2A – Evaluation of Precision Performance of Quantitative Measurement Methods; Approved Guideline- 2nd Edition
- Format for Traditional and Abbreviated 510(k)s – Guidance for Industry and FDA Staff
- In Vitro Diagnostic Devices: Guidance for the Preparation of 510(k) Submissions
## L. Test Principle:
The Calcium_2 (CA_2) method is based on the work of Michaylova and Illkova, who found that Arsenazo III could form a stable complex with calcium with high selectivity at low pH. The ADVIA 1650 system automatically predilutes the sample with saline by 5-fold. The sample predilution allows for very small sample volumes to be used for multiple tests run in random access on the analyzer. From the diluted sample, 4 uL is added to 100 uL of the reagent, mixed, and incubated for 5 minutes at 37C. Calcium ions form a colored complex with Arsenazo III, which is measured at 658 nm. Absorbances of test samples are compared with that of the Calibrator to convert signal into calcium concentrations reported to the customer. The amount of calcium present in the sample is directly proportional to the intensity of the colored complex formed.
Reaction Equation: $\mathrm{Ca^{2+}} + \mathrm{ArsenazoIII} \longrightarrow \mathrm{Ca - ArsenazoIII}$ Complex (purple)
## M. Performance Characteristics (if/when applicable):
1. Analytical performance:
a. Precision/Reproducibility:
Precision was evaluated using CLSI document EP05-A2 as a guideline.
Samples from two serum controls, two urine controls, one spiked serum pool, and one spiked, urine pool were tested. Each sample was assayed 2 times per
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run, 2 runs per day for 10 days, totaling 40 replicates using the ADVIA 1650 system. The experiment was run using one reagent lot on two systems for serum and one reagent lot on one system for urine. Medical decision levels, as well as normal range and abnormal range of the assay were challenged in this experiment.
The precision data are summarized as follows:
| | | | Within Run | | Total | |
| --- | --- | --- | --- | --- | --- | --- |
| Product | Mean (mg/dL) | N | SD | %CV | SD | %CV |
| serum (control) | 5.82 | 40 | 0.08 | 1.4 | 0.12 | 2.1 |
| serum (control) | 9.81 | 40 | 0.12 | 1.3 | 0.13 | 1.3 |
| serum (serum pool, spiked) | 13.83 | 40 | 0.07 | 0.5 | 0.11 | 0.8 |
| urine (control) | 5.95 | 40 | 0.07 | 1.2 | 0.18 | 3.0 |
| urine (control) | 11.80 | 40 | 0.11 | 0.9 | 0.18 | 1.6 |
| urine (urine pool, spiked) | 23.61 | 40 | 0.11 | 0.5 | 0.50 | 2.1 |
b. Linearity/assay reportable range:
Linearity was evaluated by comparing observed values versus expected values for 9 equally-spaced diluted samples prepared from high and low pools (separately for serum and urine). Each sample was measured in replicates of 3 using the ADVIA 1650 analyzer. The percent recovery for serum samples was within $100 - 105.5\%$ and for urine samples it was within $100 - 106.1\%$ .
In addition, the expected values (X) were plotted against the observed values (Y) and a line fit was plotted. The linear regression for serum samples is $\mathrm{Y} = 1.0026\mathrm{X} + 0.1679$ , $\mathrm{r} = 0.9998$ . For urine samples, the linear regression is $\mathrm{Y} = 0.9971\mathrm{X} = 0.3775$ , $\mathrm{r} = 0.9998$ .
The data provided supported the sponsor's claim that this assay has a reportable range of $1 - 16\mathrm{mg / dL}$ for serum samples and $1 - 32\mathrm{mg / dL}$ for urine samples.
c. Traceability, Stability, Expected values (controls, calibrators, or methods):
Traceability:
The ADVIA CA_2 method is traceable to an internal Siemens reference method (Inductively Coupled Plasma Atomic Emission), which uses reference
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materials from the National Institute of Standards and Technology (NIST), via patient sample correlation.
## Stability:
The reagent has an on-board stability of 30 days on the ADVIA 1200, ADVIA 1650/1800, and ADVIA 2400 systems. Unopened reagents are stable until the expiration date when stored at $15 - 25^{\circ}\mathrm{C}$. Reagents should not be frozen.
## d. Detection limit:
The limit of the blank (LoB) and limit of detection (LoD) were determined in accordance with the guidelines of CLSI document EP17-A using the ADVIA 1650 analyzer. Deionized water, low serum control material and low urine sample material were used to determine the LoB and LoD. Functional sensitivity of the assay was not calculated. Testing was conducted over a period of 10 days, with 2 runs per day using one system and one reagent lot. A total of 40 replicates of the blank sample, and 40 replicates of the low samples were run. The results are as follows:
LoB (serum) = 0.12 mg/dL
LoB (urine) = 0.09 mg/dL
LoD (serum) = 0.31 mg/dL
LoD (urine) = 0.25 mg/dL
The assay has a reportable range of $1 - 16\mathrm{mg / dL}$ for serum samples and $1 - 32\mathrm{mg / dL}$ for urine samples.
## e. Analytical specificity:
### i. Interference from endogenous substances:
Potential interfering substances [unconjugated and conjugated bilirubin, triglycerides (using Intralipid and avian TRIG), and hemoglobin] were spiked into two pools of human serum samples. The calcium concentrations of the two pools were $6\mathrm{mg / dL}$ and $12\mathrm{mg / dL}$. Two chelated Gadolinium contrast agents were also tested for interference, gadodiamide (Omniscan) and gadoversetamide (Optimark). Unspiked aliquots served as the control, and the analysis was performed using an ADVIA 1650 analyzer. The sponsor claims no significant interference if the $\%$ recovery is $< 10\%$ between the tested and the control samples. Percent recoveries ranged from 100 to $109.8\%$. No significant interference was observed for the following levels of interferents:
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| Interferent | Concentration |
| --- | --- |
| Hemoglobin | 1000 mg/dL |
| Bilirubin (Unconjugated) | 50 mg/dL |
| Bilirubin (Conjugated) | 50 mg/dL |
| Triglycerides (avian TRIG) | 1000 mg/dL |
| Triglycerides (Intralipid) | 1000 mg/dL |
| Gadolinium (Omniscan) | 2.0 mmol/L |
| Gadolinium (Optimark) | 2.0 mmol/L |
ii. Cross-reactivity:
None referenced
f. Assay cut-off:
Not applicable
2. Comparison studies:
a. Method comparison with predicate device:
A method comparison study was performed using 172 serum samples ranging from $1.10\mathrm{mg / dL}$ to $15.55\mathrm{mg / dL}$ and 50 urine samples ranging from 1.15 $\mathrm{mg / dL}$ to $25.76\mathrm{mg / dL}$ . Two serum samples were excluded for not being within the measuring range of the assay. The studies were performed on the ADVIA 1650 analyzer using the CA_2 assay (new device) and the CA assay (predicate).
The regression correlation is summarized as follows:
ADVIA 1650 CA_2 (Candidate Device = y) vs. ADVIA 1650 CA (Predicate Device = x)
| Sample | Regression Equation | Sy x, mg/dL | r | n | Range, m/dL |
| --- | --- | --- | --- | --- | --- |
| Serum | Y = 0.96x + 0.70 | 0.27 | 0.996 | 170 | 1.10 – 15.55 |
| Urine | Y = 0.96x + 0.29 | 0.34 | 0.998 | 50 | 1.15 – 25.76 |
b. Matrix comparison:
A matrix comparison study was performed using 25 paired serum and Li-heparinized plasma samples containing calcium across the assay range. Samples above the normal reference range were spiked. Testing was performed using one ADVIA 1650 analyzer on one reagent lot over 2 days. One sample was beyond the range of the assay and was excluded from the calculations.
The linear regression correlations are summarized as follows (plasma = y, serum = x):
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| Regression Equation | Syx, mg/dL | r | n | (x)Range, mg/dL |
| --- | --- | --- | --- | --- |
| Y = 0.996x - 0.15 | 0.24 | 0.996 | 24 | 2.00-15.28 |
# 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
# 5. Expected values/Reference range:
The serum/plasma reference range was determined by using 148 serum samples from apparently healthy adult volunteers (77 females and 71 males). The samples were assayed in duplicate on the ADVIA 1650 system using one reagent lot with non-parametric analysis to calculate the values at the 2.5 and 97.5 percentile. These results show that 95 percent of specimens fell within the calcium concentrations of $8.7 - 10.4\mathrm{mg / dL}$ (2.18 - 2.60 mmol/L), with samples ranging from $8.5 - 10.8\mathrm{mg / dL}$ (2.13 - 2.7 mmol/L). Since the equivalency between serum and plasma samples with the CA_2 method was shown in a matrix comparison study (section M.2.b), this reference range applies to both serum and plasma samples.
For urine ranges, a literature reference was used (Tietz NW. Clinical Guide to Laboratory Tests. Third Edition. Philadelphia, PA: WB Saunders Company; 1995:102-105). Expected results are as follows:
| Matrix | Range (mg) | Range (mmol) |
| --- | --- | --- |
| Serum/Plasma | 8.7 – 10.4 (mg/dL) | 2.18 – 2.60 (mmol/L) |
| Urine | 100 – 300 (mg/day) | 2.5 – 7.5 (mmol/day) |
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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.