Untrained patient capillary blood glucose measurements in physician offices
The sponsor conducted a consumer blood study involving untrained patients at three physician offices to compare the device's performance against a reference method.
Untrained patients; Sample Size: N = 97; Number of Sites: 3
Reference Method
Accuracy of capillary blood glucose measurements
Indications for Use
The ACCU-CHEK® Aviva system is designed to quantitatively measure the concentration of glucose in capillary whole blood by persons with diabetes or by health care professionals for monitoring blood glucose in the home or health care facility. The device is intended for professional use and over-the-counter sale. Professionals may use the test strip to test capillary and venous blood samples; lay use is limited to capillary whole blood testing. Testing sites include traditional fingertip site along with palm, forearm, upper arm, thigh, and calf.
Device Story
The ACCU-CHEK Aviva system is a blood glucose monitoring device for home or clinical use. It consists of a meter, reagent test strips, control solutions, and a lancing device. The user inserts a test strip into the meter, applies a capillary blood sample (0.6 μL) to the strip, and the meter provides a quantitative glucose result in 5 seconds. The system uses glucose dehydrogenase technology and electrochemical biamperometry to measure glucose concentration. It automatically distinguishes between blood samples and control solutions. The device provides plasma-equivalent results. Healthcare providers use these results to monitor glycemic control in patients with diabetes. The system includes an expiration notification feature linked to the test strip code key.
Clinical Evidence
Bench testing only. Precision studies (N=10 per level) showed CVs ranging from 1.5% to 4.21%. Linearity established across 3-677 mg/dL. Method comparison with reference method (N=97) yielded R=0.982. Matrix comparison (N=212 capillary, N=227 venous) showed high correlation (R≥0.994). Alternate site testing (AST) accuracy study (N=pooled data) showed 3.2% of results outside ISO 15197 criteria, meeting the 5% acceptance threshold. Hematocrit range tested: 23-51%.
Technological Characteristics
Glucose dehydrogenase-based electrochemical test system. Utilizes AC/DC electrical impedance for glucose measurement and environmental compensation. Features code key-based calibration, desiccated test strip vials, and automated expiration tracking. Dimensions/materials not specified. Standalone meter operation.
Indications for Use
Indicated for quantitative blood glucose monitoring in patients with diabetes. Suitable for professional use (capillary and venous blood) and over-the-counter lay use (capillary blood only). Testing sites include fingertip, palm, forearm, upper arm, thigh, and calf.
Regulatory Classification
Identification
A glucose test system is a device intended to measure glucose quantitatively in blood and other body fluids. Glucose measurements are used in the diagnosis and treatment of carbohydrate metabolism disorders including diabetes mellitus, neonatal hypoglycemia, and idiopathic hypoglycemia, and of pancreatic islet cell carcinoma.
Special Controls
*Classification.* Class II (special controls). The device, when it is solely intended for use as a drink to test glucose tolerance, is exempt from the premarket notification procedures in subpart E of part 807 of this chapter subject to the limitations in § 862.9.
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# 510(k) SUBSTANTIAL EQUIVALENCE DETERMINATION DECISION SUMMARY DEVICE ONLY TEMPLATE
A. 510(k) Number: k043474
B. Purpose For Submission:
Premarket Notification 510(k) of intention to manufacture and market the Roche Diagnostics Corp. ACCU-CHEK Aviva System.
C. Analyte: Whole Blood Glucose
D. Type of Test: Quantitative, utilizing Glucose dehydrogenase technology.
E. Applicant: Roche Diagnostics
F. Proprietary and Established Names: ACCU-CHEK® Aviva System.
G. Regulatory Information:
1. Regulation section: 21 CFR §862.1345, Glucose test system. 21 CFR §862.1660, Quality control material (assayed and unassayed).
2. Classification: Class II, Class I (reserved)
3. Product Code: NBW, LFR, JJX
4. Panel: 75 Chemistry
H. Intended Use:
1. Intended use(s):
See Indications for Use below.
2. Indication(s) for use:
The ACCU-CHEK® Aviva system is designed to quantitatively measure the concentration of glucose in capillary whole blood by persons with diabetes or by health care professionals for monitoring blood glucose in the home or health care facility. The device is indicated for professional use and over-the-counter sale. Professionals may use the test strips to test capillary and venous blood samples; lay use is limited to capillary whole blood testing. Testing sites include traditional fingertip site along with palm, forearm, upper arm, thigh, and calf.
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3. Special condition for use statement(s):
Provides plasma equivalent results.
4. Special instrument Requirements:
Roche Diagnostics Corp. ACCU-CHEK® Aviva System
I. Device Description:
The ACCU-CHEK Aviva system utilizes reagent test strips stored within a desiccated vial. A test strip is removed from the vial and inserted into the meter. Upon insertion, the meter is activated. Blood is applied to the end of the test strip and a glucose result is reported.
The ACCU-CHEK Aviva system includes:
- ACCU-CHEK Aviva meter with battery
- ACCU-CHEK test strips and code key (may be sold separately)
- ACCU-CHEK Aviva control solutions (may be sold separately)
- ACCU-CHEK Softclix lancet device (with blue cap for fingertip testing and a clear cap for non-fingertip testing)
- ACCU-CHEK Softclix lancets
J. Substantial Equivalence Information:
1. Predicate device name(s):
Roche Diagnostics, Corp. ACCU-CHEK Advantage System
2. Predicate K number(s): K010362 and K032552
3. Comparison with Predicate:
The sponsor claims that the Roche Diagnostics ACCU-CHEK Aviva system is substantially equivalent to other products in commercial distribution intended for similar use. Most notably it is substantially equivalent to the currently marketed Roche Diagnostics ACCU-CHEK Advantage system.
Similarities
| Topic | Comment |
| --- | --- |
| Intended Use | Both systems are intended for testing glucose in whole blood by persons with diabetes or by health care professionals in the home or in health care facilities. |
| Closed system | Each system’s test strips and controls are designated to be used only with that |
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# Differences
| Topic | ACCU-CHEK Aviva | ACCU-CHEK Advantage |
| --- | --- | --- |
| Indication of control solution results | Automatically distinguishes control solution from whole blood samples. | User must identify (flag) the control solution result manually. |
| Test sample volume | 0.6 μL | 4.0 μL |
| Test time | 5 seconds | 26 seconds (Comfort Curve test strips) |
| Expiration | In addition to information included in labeling, the code key contains expiration date of associated test strips. System informs user when code key has expired. | No notification of expiration beyond that included in labeling. |
| Test strip technology | The system utilizes both AC/DC electrical impedance information. | The system utilizes electrical biamperometry information. |
| Labeling instructions regarding expected results | The normal fasting blood glucose range for an adult without diabetes is 74-106 mg/dL. Two hours after meals, the blood glucose range for an adult without | The normal fasting adult blood glucose range for a non-diabetic is 70-105 mg/dL. One to tow hours after meals, normal blood glucose levels should be |
| | in the first 24 hours. | in the first 24 hours. |
| Analyses of the test results | In order to assess the test results, the test strip is used to determine the test results. The test strip is used to determine the test results in a 100% confidence interval. | In order to assess the test results, the test strip is used to determine the test results in a 50% confidence interval. |
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| | diabetes is less than 140 mg/dL. For people with diabetes: please consult your doctor for the blood glucose range appropriate for you. | less than 140 mg/dL. Doctors will determine the range that is appropriate for their individual patients. |
| --- | --- | --- |
## K. Standard/Guidance Document Referenced (if applicable):
NCCLS EP6-A standard, “Evaluation of the Linearity of Quantitative Measurement Procedures: A Statistical Approach.”
ISO 15197 standard minimum acceptable accuracy acceptance criteria (within ±15 mg/dL for <75 mg/dL and within ±20% for ≥75 mg/dL)
QA 43-SOP Procedure for Establishing the Precision Claim of Blood Glucose Systems.
## L. Test Principle:
The ACCU-CHEK Aviva System utilizes Glucose dehydrogenase technology. The enzyme glucose dehydrogenase converts the glucose in a blood sample to gluconolactone. This reaction liberates an electron that reacts with a coenzyme electron acceptor, the oxidized form of the mediator hexacyanoferrate (III), forming the reduced form of the mediator hexacyanoferrate (III). The test strip employs the electrochemical principle of biamperometry. The meter applies a voltage between two identical electrodes, which causes the reduced mediator formed during the incubation period to be reconverted to an oxidized mediator. This generates a small current that is read by the meter and reported as the glucose result.
## M. Performance Characteristics (if/when applicable):
### 1. Analytical performance:
#### a. Precision/Reproducibility:
The sponsor indicated precision studies were assessed by taking venous blood samples that were treated with EDTA, and spiking these samples to generate 5 different levels of glucose concentration for the test. Each of the samples was measured 10 times. Below are the Glucose Concentration Ranges and results for each level that was measured.
| Blood | Level 1
30-50 mg/dL | Level 2
51-110 mg/dL | Level 3
111-150 mg/dL | Level 4
151-250 mg/dL | Level 5
251-400 mg/dL |
| --- | --- | --- | --- | --- | --- |
| N | 10 | 10 | 10 | 10 | 10 |
| Mean | 38 | 107 | 143 | 245 | 341 |
| SD | 1.6 | 3.0 | 3.7 | 4.9 | 7.5 |
| CV | 4.21 | 2.8 | 2.6 | 2.0 | 2.2 |
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# Day-to-Day precision also known as Between Day Precision
The Day-to-Day precision according to the sponsor was determined by preparing three control and blood solutions of Low, Normal and High. The sponsor indicated that the protocol used for this comparison was based on requirements outlined in QA 43-SOP Procedure for Establishing the Precision Claim of Blood Glucose Systems. As an outcome of this study, the following precision claim is included in the labeling of the product:
| Control Solution | Low (mg/dL) | Mid (mg/dL) | High (mg/dL) |
| --- | --- | --- | --- |
| N | 10 | 10 | 10 |
| Mean | 41 | 130 | 306 |
| SD | 1.1 | 2.4 | 5.0 |
| CV | 2.68 | 1.8 | 1.5 |
| Blood | Low (mg/dL) | Mid (mg/dL) | High (mg/dL) |
| --- | --- | --- | --- |
| N | 10 | 10 | 10 |
| Mean | 38 | 143 | 341 |
| SD | 1.6 | 3.7 | 7.5 |
| CV | 4.21 | 2.6 | 2.2 |
# b. Linearity/assay reportable range:
The linearity was established by diluting specimens to cover the range of $3 - 677\mathrm{mg / dL}$
For linearity, the sponsor chose to use the ISO 15197 Standard minimum acceptable accuracy acceptance criteria (within $\pm 15\mathrm{mg / dL}$ for glucose results $< 75\mathrm{mg / dL}$ , and within $\pm 20\%$ for glucose results $\geq 75\mathrm{mg / dL}$ ) see low end data below.
| Test Strip Lot | Hitachi HK Reference Value (mg/dL) | Meter Response (mg/dL) | Consumer would see |
| --- | --- | --- | --- |
| 74 | 3.1 | 1 | LO |
| 74 | 3.1 | 1 | LO |
| 72 | 3.1 | 1 | LO |
| 72 | 3.1 | 1 | LO |
| 72 | 3.1 | 1 | LO |
| 72 | 3.1 | 1 | LO |
| 73 | 3.1 | 1 | LO |
| 73 | 3.1 | 1 | LO |
| 74 | 3.1 | 2 | LO |
| 74 | 3.1 | 2 | LO |
| 73 | 3.1 | 2 | LO |
| 73 | 3.1 | 2 | LO |
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| Test Strip Lot | Hitachi HK Reference Value (mg/dL) | Meter Response (mg/dL) | Consumer would see |
| --- | --- | --- | --- |
| 72 | 13.4 | 1 | LO |
| 72 | 13.4 | 3 | LO |
| 72 | 13.4 | 3 | LO |
| 72 | 13.4 | 3 | LO |
| 72 | 13.4 | 4 | LO |
| 72 | 13.4 | 5 | LO |
| 72 | 13.4 | 5 | LO |
| 72 | 16.2 | 5 | LO |
| 72 | 13.4 | 6 | LO |
| 72 | 16.2 | 6 | LO |
| 72 | 18.4 | 6 | LO |
| 72 | 18.4 | 6 | LO |
| 72 | 16.2 | 7 | LO |
| 72 | 16.2 | 7 | LO |
| 72 | 16.2 | 7 | LO |
| 72 | 16.2 | 7 | LO |
| 72 | 18.4 | 7 | LO |
| 72 | 18.4 | 7 | LO |
| 72 | 16.2 | 8 | LO |
| 72 | 18.4 | 8 | LO |
| 72 | 18.4 | 8 | LO |
| 72 | 20.8 | 8 | LO |
| 72 | 18.4 | 9 | LO |
| 72 | 18.4 | 9 | LO |
| 72 | 20.4 | 10 | 10 |
| 72 | 20.4 | 10 | 10 |
| 72 | 20.4 | 10 | 10 |
| 72 | 20.8 | 10 | 10 |
| 72 | 20.8 | 10 | 10 |
| 72 | 20.4 | 11 | 11 |
| 72 | 20.8 | 11 | 11 |
| 72 | 20.8 | 11 | 11 |
| 72 | 20.4 | 12 | 12 |
| 72 | 20.4 | 12 | 12 |
| 72 | 20.8 | 12 | 12 |
| 72 | 20.8 | 12 | 12 |
| 72 | 20.4 | 13 | 13 |
| 73 | 13.4 | 7 | LO |
| 73 | 13.4 | 7 | LO |
| 73 | 13.4 | 7 | LO |
| 73 | 13.4 | 9 | LO |
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| Test Strip Lot | Hitachi HK Reference Value (mg/dL) | Meter Response (mg/dL) | Consumer would see |
| --- | --- | --- | --- |
| 73 | 18.4 | 10 | 10 |
| 73 | 13.4 | 11 | 11 |
| 73 | 16.2 | 11 | 11 |
| 73 | 16.2 | 11 | 11 |
| 73 | 16.2 | 11 | 11 |
| 73 | 18.4 | 11 | 11 |
| 73 | 18.4 | 11 | 11 |
| 73 | 18.4 | 11 | 11 |
| 73 | 13.4 | 12 | 12 |
| 73 | 13.4 | 12 | 12 |
| 73 | 16.2 | 12 | 12 |
| 73 | 16.2 | 12 | 12 |
| 73 | 16.2 | 12 | 12 |
| 73 | 18.4 | 12 | 12 |
| 73 | 18.4 | 12 | 12 |
| 73 | 13.4 | 13 | 13 |
| 73 | 16.2 | 13 | 13 |
| 73 | 16.2 | 13 | 13 |
| 73 | 18.4 | 13 | 13 |
| 73 | 18.4 | 13 | 13 |
| 73 | 20.8 | 13 | 13 |
| 73 | 20.8 | 13 | 13 |
| 73 | 20.4 | 14 | 14 |
| 73 | 20.4 | 14 | 14 |
| 73 | 20.8 | 14 | 14 |
| 73 | 20.8 | 14 | 14 |
| 73 | 20.4 | 15 | 15 |
| 73 | 20.4 | 15 | 15 |
| 73 | 20.8 | 15 | 15 |
| 73 | 20.8 | 15 | 15 |
| 73 | 20.4 | 16 | 16 |
| 73 | 20.4 | 16 | 16 |
| 73 | 20.8 | 16 | 16 |
| 73 | 20.4 | 17 | 17 |
| 73 | 20.4 | 17 | 17 |
| 74 | 13.4 | 6 | LO |
| 74 | 13.4 | 7 | LO |
| 74 | 13.4 | 8 | LO |
| 74 | 13.4 | 8 | LO |
| 74 | 13.4 | 9 | LO |
| 74 | 13.4 | 9 | LO |
| 74 | 13.4 | 11 | 11 |
| 74 | 16.2 | 12 | 12 |
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Page 8 of 11
| Test Strip Lot | Hitachi HK Reference Value (mg/dL) | Meter Response (mg/dL) | Consumer would see |
| --- | --- | --- | --- |
| 74 | 16.2 | 12 | 12 |
| 74 | 16.2 | 12 | 12 |
| 74 | 16.2 | 12 | 12 |
| 74 | 18.4 | 12 | 12 |
| 74 | 18.4 | 12 | 12 |
| 74 | 13.4 | 13 | 13 |
| 74 | 16.2 | 13 | 13 |
| 74 | 16.2 | 13 | 13 |
| 74 | 16.2 | 13 | 13 |
| 74 | 18.4 | 13 | 13 |
| 74 | 18.4 | 13 | 13 |
| 74 | 18.4 | 13 | 13 |
| 74 | 18.4 | 13 | 13 |
| 74 | 16.2 | 14 | 14 |
| 74 | 18.4 | 14 | 14 |
| 74 | 20.4 | 14 | 14 |
| 74 | 20.8 | 14 | 14 |
| 74 | 20.4 | 15 | 15 |
| 74 | 20.4 | 15 | 15 |
| 74 | 20.8 | 15 | 15 |
| 74 | 20.8 | 15 | 15 |
| 74 | 20.8 | 15 | 15 |
| 74 | 20.4 | 16 | 16 |
| 74 | 20.4 | 16 | 16 |
| 74 | 20.4 | 16 | 16 |
| 74 | 20.8 | 16 | 16 |
| 74 | 20.8 | 16 | 16 |
| 74 | 20.4 | 17 | 17 |
| 74 | 20.8 | 17 | 17 |
| | | | |
| count | 132 | 132 | |
| mean | 16.5 | 10 | |
| min | 3.12 | 1 | |
| max | 20.8 | 17 | |
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Page 9 of 11

ACCU-CHEK Aviva (K#043474) - Low End Performance Data
c. Traceability (controls, calibrators, or method):
During the control solution production process, anhydrous glucose material is weighed out to within 0.2% of the target weight. Prior to bottling the final product a sample of the solution is verified via a Hitachi hexokinase method.
d. Detection limit:
See linearity above
10 – 600 mg/dL
0.6 to 33.3 mmol/L
e. Analytical specificity:
According the sponsor interference testing was conducted to determine the effect of select endogenous and exogenous substances. Testing was conducted using a glucose depleted normal human serum matrix spiked with the potential interferents at the acceptable upper concentrations. The recommended test level was based on EP7-A NCCLS guidelines and literature references. This study included a total of 177 compounds which were screened, of which the following will be claimed as limitation:
- Galactose
- Lipidemia (Triglycerides)
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- Maltose
- Xylose (Xylose will not be tested in whole blood but will be listed as a limitation based on serum results.
The below compounds when determined to be in excess of their limitations, may produce elevated results:
1. Galactose > 10 mg/dL can give falsely elevated test results.
2. Maltose > 13 mg/dL delivered intravenously can give falsely elevated test results.
3. Lipids (Triglycerides) > 4800 mg/dL can give falsely elevated test results
f. Assay cut-off:
Not Applicable
2. Comparison studies:
a. Method comparison with predicate device:
A consumer blood study comparisons, to the Reference Method was conducted, where capillary blood sample results obtained by untrained patients from three physician offices gave the following results.
$$
N = 97
$$
$$
Y = 0.957x + 2.7
$$
$$
R = 0.982
$$
$$
\text{Range} = 73 - 330 \text{ mg/dL}
$$
b. Matrix comparison:
Studies for matrix comparison were conducted at one physician site for capillary blood samples and three physician office sites for venous blood samples with the following results:
| Capillary Blood | Venous Blood |
| --- | --- |
| N = 212 | N = 227 |
| Y = 0.984x + 2.2 | Y = 0.993x + 0.3 |
| R = 0.996 | R = 0.994 |
| Range = 26 – 461 mg/dL | Range = 32 – 583 mg/dL |
Alternate Site Testing
The sponsor demonstrated Alternate Site Testing (AST) of the palm, forearm, upper arm, thigh, and calf with the clearance of 510(k) submission K022171 Roche Diagnostics ACCU-CHEK Compact System. The sponsor has AST studies on two (2) sites with the new device, comparing to the fingertip as the reference method in this submission.
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In the AST accuracy study performed by the sponsor, a trained technician performed a minimum of four capillary sticks on each patient to include a capillary fingerstick as a reference, capillary forearm stick, capillary palm thenar (thumb side) and a second capillary fingerstick used and hematocrit sample. As indicated in the above previously cleared submission K022171, the sponsor has included label warnings not recommending AST testing during periods of rapidly decreasing or increasing blood glucose levels.
The percentage of individual results falling outside 15 mg/dL for glucose results < 75 mg/dL and 20% for glucose results ≥ 75 mg/dL was 3.2% for the pooled data, which was not significantly higher than the 5% acceptance criteria. The Hematocrit range obtained was 23-51% with a mean of 40.5%.
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 normal fasting blood glucose range for an adult without diabetes as related to plasma is 74-106 mg/dL¹. Two hours after meals, blood glucose range for a non-diabetic is less than 140 mg/dL². For people with diabetes please consult your diabetes team for the blood glucose range appropriate for you.
1. Stedman, TL, Stedman’s Medical Dictionary, 27th Edition, 1999, pg. 2082.
2. American Diabetes Association, Clinical Practice Recommendation Guidelines 2003, Diabetes Care, Vol. 26. Supplement 1, p. S22.
N. Proposed Labeling:
The labeling is sufficient and it satisfies the requirements of 21 CFR Part 809.10
O. Conclusion:
The submitted material 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.