K060280 · Iris International, Inc. · JIL · Jun 13, 2006 · Clinical Chemistry
Device Facts
Record ID
K060280
Device Name
ICHEM 100 URINE CHEMISTRY ANALYZER
Applicant
Iris International, Inc.
Product Code
JIL · Clinical Chemistry
Decision Date
Jun 13, 2006
Decision
SESE
Submission Type
Traditional
Regulation
21 CFR 862.1340
Device Class
Class 2
Indications for Use
The iChem100 Urine Chemistry Analyzer (iChem100) is a semi-automated benchtop urine chemistry analyzer intended for the in vitro measurement of the following analytes: glucose, protein, bilirubin, urobilinogen, pH, specific gravity, blood, ketones, nitrite, leukocyte esterase, ascorbic acid, and color. The iChem100 is intended for use only with iChem 10 SG Urine Chemistry Strips provided by Iris Diagnostics and is intended for use exclusively by healthcare professionals. These measurements are useful in the evaluation of renal, urinary, and metabolic disorders.
Device Story
The iChem 100 is a semi-automated benchtop urine chemistry analyzer for clinical use. The operator dips an iChem 10 SG test strip into a urine sample and places it on the analyzer tray. The device automatically moves the strip to an internal optical block. It uses reflectance spectroscopy via a CMOS camera to analyze the colorimetric changes on the reagent pads. The system provides qualitative and semi-quantitative results for 11 analytes. Results are used by healthcare professionals to evaluate renal, urinary, and metabolic disorders. The inclusion of an ascorbic acid reagent pad serves as a warning for potential interference with other test results.
Clinical Evidence
Bench testing only. Correlation studies compared the iChem 100 to the predicate device. Additional non-clinical studies evaluated precision and linearity to demonstrate acceptable performance for the intended use.
Technological Characteristics
Semi-automated benchtop urine analyzer. Sensing principle: reflectance spectroscopy using a CMOS camera. Microprocessor-controlled. Compatible with iChem 10 SG test strips. Analyte measurement includes glucose, protein, bilirubin, urobilinogen, pH, specific gravity, blood, ketones, nitrite, leukocyte esterase, ascorbic acid, and color.
Indications for Use
Indicated for healthcare professionals to perform in vitro qualitative and semi-quantitative urine chemistry analysis (glucose, protein, bilirubin, urobilinogen, pH, specific gravity, blood, ketones, nitrite, leukocyte esterase, ascorbic acid, and color) for the evaluation of renal, urinary, and metabolic disorders.
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
AUTION JET AJ-4270, IRIS Diagnostics (k030600)
Submission Summary (Full Text)
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# 510(k) SUBSTANTIAL EQUIVALENCE DETERMINATION DECISION SUMMARY INSTRUMENT ONLY TEMPLATE
A. 510(k) Number:
k060280
B. Purpose for Submission:
Modified 510(k): The sponsor wishes to establish clear ownership of their earlier cleared test system which will now be marketed under a new name. Additionally, the analyzer software was modified since the initial clearance of the product.
C. Manufacturer and Instrument Name:
Iris Diagnostics facility in Marburg, Germany, iChem 100 Urine Chemistry Analyzer
(IRIS is an abbreviation for International Remote Imaging Systems.)
D. Type of Test(s) Performed:
Qualitative and Semi-quantitative urine chemistry assays
E. System Description
1. Device Description:
This is a semi-automated benchtop instrument, intended exclusively for use with the sponsors earlier cleared test strips (k030600). Components include a keyboard, transport mechanism, display screen, printer, and barcode reader.
Test strips are manually dipped into the urine sample and placed on the instrument transport belt. The strip is transported into the instrument where readings of each chemistry are taken at timed intervals. Readings are converted to concentrations which are displayed to the operator. There are also options to print results or to transmit them to a Laboratory Information System.
2. Principles of Operation:
The technology utilized in the instrument is reflectance spectroscopy. The reflectance densitometer reads at three wavelengths; 450, 530, and 625 nm. Instead of using a photodiode detector to measure the reflected light, the instrument utilizes a complementary metal oxide semiconductor (CMOS) image sensor. This technology is
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well established and is the same as the sponsor’s earlier cleared instrument.
3. **Modes of Operation:**
The instrument allows both batch and stat modes.
4. **Specimen Identification:**
Specimens are identified by a barcode reader or by manual input via the keyboard. A work list can also be created by the operator.
5. **Specimen Sampling and Handling:**
The iChem reagent strip is manually dipped into a urine specimen then placed on the instrument transport belt. All steps that follow are automatically controlled by the instrument’s software.
6. **Calibration:**
No external calibration strips or procedures are required. Prior to each measurement the device is calibrated using a one-point optics alignment calibration. If readings fall outside of acceptable limits, a secondary standard is moved into place to perform a two-point calibration. The approach to calibration is appropriate for the CMOS image sensor technology.
7. **Quality Control (QC):**
A control file may be set up by the operator to record the lot and expiration of the control material, and to establish the frequency for running controls. The software also allows manual microscopic examination results to be entered into the system.
Commercially available control materials (negative and known positive concentrations) are recommended for use according to local, state, and federal regulatory guidelines. Specific controls are not identified.
8. **Software:**
FDA has reviewed applicant’s Hazard Analysis and software development processes for this line of product types:
Yes ☑ or No ☐
The following sections are presented in the submission, and they appear adequate:
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Level of Concern – The sponsor has classified their device as a Moderate Level of Concern.
Software Description – An overview of the features controlled by the software and the software operating environment is present.
Hazard Analysis – A summary of the Failure Mode and Effects Analysis activity is presented. It lists hardware and software hazards, severity assessments, and mitigations. It identifies the cause(s) of hazards, methods of control (e.g., alarm, hardware design), corrective measures taken, including an explanation of the aspects of the device design/requirements, that eliminate, reduce, or warn of a hazardous event, and verification that the method of control was implemented correctly.
Software Requirements Specification (SRS) – A document listing functional requirements for the software, e.g., interface, performance, or functional needs is provided.
Architecture Design Chart – A design specification document is provided.
Traceability Analysis – Traceability among requirements, specifications, identified hazards and mitigations, and Verification and Validation testing is provided.
Software Development Environment Description – A summary of the software development life cycle and the processes that are in place to manage the various life cycle activities is provided, e.g., changes or adjustments to software after released into market. Additionally, an annotated list of control documents generated during the development process is present. It includes a summary of the configuration management and maintenance activities. (The waterfall process model of development was used.)
Verification and Validation Documentation – A description of V&V activities at the unit, integration, and system level are present. System level test protocols, including pass/fail criteria, and test results are also provided.
Revision Level History – The sponsor has provided the Revision history log, including release version number and date.
Unresolved Anomalies – The sponsor indicates there are no known unresolved anomalies at the time of this submission. Therefore, a list of remaining software anomalies, annotated with an explanation of the impact on safety or effectiveness, including operator usage and human factors is not needed.
F. Regulatory Information:
1. Regulation section:
Class II:
21CFR §862.1340 – Urinary glucose (nonquantitative) test system
21CFR §864.6550 – Occult blood test
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Class I:
21 CFR §862.1095 - Ascorbic acid test system
21 CFR §862.1115 - Urinary bilirubin and its conjugates (nonquantitative) test system.
21 CFR §862.1435 - Ketones (nonquantitative) test system
21 CFR §862.1510 - Nitrite (nonquantitative) test system
21 CFR §862.1550 - Urinary pH (nonquantitative) test system
21 CFR §862.1645 - Urinary protein or albumin (nonquantitative) test system
21 CFR §862.1785 - Urinary urobilinogen (nonquantitative) test system
21 CFR §862.2300 - Colorimeter, photometer, or spectrophotometer for clinical use
21 CFR §864.7675 - Leukocyte peroxidase test
21 CFR §862.2900 - Automated urinalysis system
2. Classification:
Class II (blood and glucose) and the remainder are Class I (The class I analyzer is subject to review because Class II analytes are run on it. Class I analytes are reviewed because they are part of a device which includes class II devices.)
3. Product code:
Class II: JIL, JIO
Class I: JMA, JJB, JIN, JMT, CEN, JIR, CDM, JJQ, LJX, KQO
4. Panel:
75 (Chemistry)
G. Intended Use:
1. Indication(s) for use:
The iChem100 Urine Chemistry Analyzer (iChem100) is a semi-automated benchtop urine chemistry analyzer intended for the in vitro measurement of the following analytes: glucose, protein, bilirubin, urobilinogen, pH, specific gravity, blood, ketones, nitrite, leukocyte esterase, ascorbic acid, and color. The iChem100 is intended for use only with iChem 10 SG Urine Chemistry Strips provided by Iris Diagnostics and is intended for use exclusively by healthcare professionals.
These measurements are useful in the evaluation of renal, urinary, and metabolic disorders.
2. Special conditions for use statement(s):
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For professional use.
## H. Substantial Equivalence Information:
1. Predicate device name(s):
AUTION JET AJ-4270, IRIS Diagnostics
2. Predicate 510(k) number(s):
k030600
3. Comparison with predicate:
Both devices measure various chemistry parameters in urine, are semi-automated analyzers, and utilize the same test methodology.
The site where the instruments are manufactured are different, however both are manufactured by IRIS Diagnostics, International.
## I. Standard/Guidance Document Referenced (if applicable):
The sponsor references the following standards:
EN 51010A-1 Safety requirements for electrical equipment, Part 1, General requirements.
CAN/CSA-C22.2, No. 1010.1, Safety requirements for electrical equipment, Part 1, General requirements.
## J. Performance Characteristics:
1. Analytical performance: To demonstrate performance of the iChem analyzer the sponsor presents data from two types of studies, as appropriate. (Not all studies are done for each analyte.)
a. Accuracy:
Results from the iChem100 were compared to those from a commercially available system, the AUTION JET AJ-4270 test system. Ascorbic Acid was compared to the UrinQuic device.
Testing was performed by skilled personnel at IRIS. Clinical Urine samples for the study were obtained from a local medical center. The samples include both normal and abnormal levels of urine analytes.
Results obtained in these correlation studies are presented in block tables according to the
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ranges reported by the individual systems. Additionally, values obtained from all urine samples were referred to as either negative (normal) or positive (abnormal) findings. These data are presented in 2X2 tables for comparison of the two assay methods.
## Glucose Correlation Study: Categorized by Reporting Ranges (mg/dL)
| | | Arkray Result | | | | | | |
| --- | --- | --- | --- | --- | --- | --- | --- | --- |
| | | Neg. | @ 30/50 | @ 70/100 | @ 150/200 | @ 300/500 | @ > 1000 | Total |
| iChem Result | Neg. | 156 | 0 | 0 | 0 | 0 | 0 | 156 |
| | @ 50 | 2 | 5 | 5 | 0 | 0 | 0 | 12 |
| | @ 150 | 0 | 0 | 1 | 3 | 4 | 3 | 11 |
| | @ 500 | 0 | 0 | 0 | 0 | 1 | 5 | 6 |
| | @ > 1000 | 0 | 0 | 0 | 0 | 0 | 3 | 3 |
| | Total | 158 | 5 | 6 | 3 | 5 | 11 | 188 |
## Glucose Correlation Study: Categorized by Negative/Positive Findings
| | Arkray Negative | Arkray Positive | Total |
| --- | --- | --- | --- |
| iChem Negative | 156 | 0 | 156 |
| iChem Positive | 2 | 30 | 32 |
| Total | 158 | 30 | 188 |
## Blood Correlation Study: Categorized by Reporting Ranges (mg/dL)
| | | Arkray Result | | | | | |
| --- | --- | --- | --- | --- | --- | --- | --- |
| | | 0 | @ 0.03 | @ 0.06/0.1 | @ 0.2/0.5 | @ 1 | Total |
| iChem Results | 0 | 147 | 1 | 2 | 0 | 0 | 150 |
| | @ 0.03 | 4 | 12 | 3 | 1 | 0 | 20 |
| | @ 0.2 | 0 | 1 | 0 | 3 | 2 | 6 |
| | @ 1.0 | 1 | 0 | 0 | 2 | 9 | 12 |
| | Total | 152 | 14 | 5 | 6 | 11 | 188 |
## Blood Correlation Study: Categorized by Negative/Positive Findings
| | Arkray Negative | Arkray Positive | Total |
| --- | --- | --- | --- |
| iChem Negative | 147 | 3 | 150 |
| iChem Positive | 5 | 33 | 38 |
| Total | 152 | 36 | 188 |
The following analytes were tested using both spiked samples (compared to expected spiked
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value) and clinical samples compared to results from a predicate device:
## Ascorbic Acid
| iChem 100 | | Spiked Concentration (mg/dL) | | | | |
| --- | --- | --- | --- | --- | --- | --- |
| | | 0 | 10 | 20 | 30 | 40 |
| | 40 | | | 3 | 25 | 40 |
| | 20 | | | 60 | 38 | 23 |
| | Neg | 63 | 63 | | | |
| iChem 100 | | Predicate Device (mg/dL) | | |
| --- | --- | --- | --- | --- |
| | | Neg | 20 | 40 |
| | 40 | | 3 | 1 |
| | 20 | 2 | 9 | |
| | Neg | 89 | | |
## Bilirubin
| iChem 100 | | Spiked Concentration (mg/dL) | | | | | |
| --- | --- | --- | --- | --- | --- | --- | --- |
| | | 0 | 0.8 | 1 | 1.5 | 2 | 4 |
| | 4 | | | | | | 9 |
| | 2 | | | | 22 | 53 | 53 |
| | 1 | | | | 30 | 7 | 1 |
| | Neg | 63 | 63 | 63 | 27 | 3 | |
| iChem 100 | | Predicate Device (mg/dl) | | | | |
| --- | --- | --- | --- | --- | --- | --- |
| | | 0 | 0.5-1.0 | 2.0 | 6.0-10.0 | >10.0 |
| | 4 | 1 | | 3 | | 1 |
| | 2 | 8 | 3 | 14 | | |
| | 1 | 6 | 7 | | | |
| | Neg | 141 | 2 | 2 | | |
## Ketones
| iChem 100 | | Spiked Concentration (mg/dL) | | | | | |
| --- | --- | --- | --- | --- | --- | --- | --- |
| | | 0 | 20 | 25 | 30 | 100 | 300 |
| | 300 | | | | | 2 | 55 |
| | 100 | | | | | 59 | 2 |
| | 25 | | 42 | 53 | 48 | | |
| | Neg | 54 | | | | | |
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| iChem 100 | | Predicate Device (mg/dL) | | | |
| --- | --- | --- | --- | --- | --- |
| | | 0 | 5-60 | 80-100 | >150 |
| | 300 | | | | |
| | 100 | | | | |
| | 25 | 4 | 16 | | |
| | Neg | 167 | 1 | | |
## Leukocytes
| iChem 100 | | Spiked Concentration (WBC/μL) | | | | | |
| --- | --- | --- | --- | --- | --- | --- | --- |
| | | 0 | 15 | 25 | 50 | 75 | 500 |
| | 500 | | | 11 | 9 | 10 | 57 |
| | 75 | | 6 | 29 | 24 | 22 | 6 |
| | 25 | | 57 | 26 | 32 | 27 | |
| | Neg | 63 | | 1 | | 4 | |
| iChem 100 | | Predicate Device (WBC/μL) | | | | |
| --- | --- | --- | --- | --- | --- | --- |
| | | 0 | 25 | 75 | 250 | 500 |
| | 500 | | | 3 | 5 | 11 |
| | 75 | | 1 | | | 1 |
| | 25 | 17 | 3 | 2 | 1 | |
| | Neg | 142 | | 2 | | |
## Nitrite
| iChem 100 | | Spiked Concentration (mg/dL) | | | |
| --- | --- | --- | --- | --- | --- |
| | | 0 | 0.03 | 0.05 | 0.08 |
| | Pos | | 32 | 44 | 59 |
| | Neg | 57 | 32 | 19 | 4 |
| iChem 100 | | Predicate Device (mg/dL) | | | |
| --- | --- | --- | --- | --- | --- |
| | | 0 | 0.03 | 0.05 | 0.08 |
| | Pos | 2 | 31 | | |
| | Neg | 154 | 1 | | |
## pH
| iChem 100 | | Spiked Concentration (pH meter) | | | | |
| --- | --- | --- | --- | --- | --- | --- |
| | | 5 | 6 | 7 | 8 | 9 |
| | 5.0 | 10 | | | | |
| | 6.0 | | 2 | | | |
| | 7.0 | | 8 | | | |
| | 8.0 | | | 10 | | |
| | 9.0 | | | | 10 | 10 |
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| iChem 100 | | Predicate Device (pH) | | | | |
| --- | --- | --- | --- | --- | --- | --- |
| | | 5.0-5.5 | 6.0-6.5 | 7.0-7.5 | 8.0-8.5 | 9.0 |
| | 9.0 | | | 1 | | 2 |
| | 8.0 | | | 8 | | |
| | 7.0 | | 15 | 5 | | |
| | 6.0 | 10 | 34 | 1 | | |
| | 5.0 | 87 | 25 | | | |
## Protein
| iChem 100 | | Spiked Concentration (mg/dL) | | | | | |
| --- | --- | --- | --- | --- | --- | --- | --- |
| | | 0 | 15 | 30 | 50 | 100 | 500 |
| | 500 | | | | | | 42 |
| | 100 | | | | 14 | 42 | |
| | 30 | | 5 | 42 | 28 | | |
| | Neg | 42 | 37 | | | | |
| iChem 100 | | Predicate Device (mg/dL) | | | |
| --- | --- | --- | --- | --- | --- |
| | | 0 | 10-70 | 100-200 | 300-600 |
| | 500 | | | | 3 |
| | 100 | | 2 | 15 | 2 |
| | 30 | 13 | 37 | 1 | |
| | Neg | 112 | 2 | 1 | |
## Specific Gravity
| iChem 100 | | Spiked Concentration (refractometer) | | | | | | | |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| | | 1.000 | 1.005 | 1.010 | 1.015 | 1.020 | 1.025 | 1.030 | 1.035 |
| | 1.000 | 10 | | | | | | | |
| | 1.005 | | 10 | 8 | | | | | |
| | 1.010 | | | 2 | 2 | | | | |
| | 1.015 | | | | 8 | 2 | | | |
| | 1.020 | | | | | 8 | 1 | | |
| | 1.025 | | | | | | 9 | 3 | |
| | 1.030 | | | | | | | 7 | 10 |
| | 1.035 | | | | | | | | |
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10
| iChem 100 | | Predicate Device (Sp. Grav.) | | | | | | | |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| | | 1.000 | 1.005 | 1.010 | 1.015 | 1.020 | 1.025 | 1.030 | >1.030 |
| | 1.035 | | | | | | | 3 | 1 |
| | 1.030 | | | | | | 1 | 9 | 9 |
| | 1.025 | | 1 | | | 3 | 5 | 13 | 9 |
| | 1.020 | | | | | 8 | 14 | 3 | 5 |
| | 1.015 | | | | 1 | 16 | 8 | 1 | |
| | 1.010 | | | 1 | 12 | 12 | | | |
| | 1.005 | | 15 | 8 | 9 | 5 | 2 | | |
| | 1.000 | | 3 | | | | | | |
## Urobilinogen
| iChem 100 | | Spiked Concentration (mg/dL) | | | | | | |
| --- | --- | --- | --- | --- | --- | --- | --- | --- |
| | | 0 | 1 | 2 | 3 | 4 | 8 | 12 |
| | 12 | | | | | | | |
| | 8 | | | | 57 | 56 | 57 | 58 |
| | 4 | | 5 | 24 | 1 | 3 | | |
| | 2 | | 52 | 9 | | 1 | | |
| | Neg | 63 | 23 | 1 | | | | |
| iChem 100 | | Predicate Device (mg/dL) | | | | |
| --- | --- | --- | --- | --- | --- | --- |
| | | 0 | 2-3 | 4-6 | 8-12 | >12 |
| | 12 | | | | | 1 |
| | 8 | 1 | 2 | 3 | 1 | |
| | 4 | | 4 | | | |
| | 2 | 12 | 5 | | | |
| | Neg | 157 | 2 | | | |
## Color
Since the predicate device and the subject device differ in the number of colors for reporting results (16 for the predicate and 10 for the subject device), the sponsor compared the color on the two devices in 629 clinical samples:
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| iChem 100 | | Predicate Device (color) | | | | | | | |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| | | Colorless | Light Yellow | Yellow/Dark Yellow | Light Orange/Orange/Dark Orange | Light Red/Red/Dark Red | Light Brown/Brown/Dark Brown | Green | Violet/Blue |
| | Black | | | | | | 1 | | |
| | Green | | | | | | | | |
| | Brown | | | | | | | | |
| | Red Brown | | | | | | 1 | | |
| | Red | | | 18 | | 6 | | | |
| | Orange | | | 2 | 1 | | 3 | | |
| | Amber | 4 | 2 | 111 | 2 | 11 | 8 | | |
| | Yellow | 52 | 72 | 305 | 4 | 1 | | | |
| | Straw | 12 | 4 | 6 | | | | | |
| | Colorless | | | 3 | | | | | |
b. Precision/Reproducibility:
Two levels of commercially available control (normal and abnormal concentrations of each analyte) were analyzed to estimate precision of the test system. Testing was performed in the sponsor's own laboratory by their employees.
To estimate total imprecision, 23 abnormal samples and 20 normal samples were run over a 13 day period. To estimate within-run imprecision 20 normal and 20 abnormal samples were analyzed in a single run. Testing was performed on the candidate device, and the candidate device prior to the software modifications. Testing was done in the sponsor's laboratory by trained laboratory technicians.
Total Precision, Candidate Device at Normal Levels
| Analyte | Control Range | % Reflectance | Standard deviation | % CV | 95% Confidence Interval for CV |
| --- | --- | --- | --- | --- | --- |
| Bilirubin | Neg | 63.6 | 2.1 | 3.3 | [2.6-4.7] |
| Urobilinogen | Norm | 57.7 | 1.9 | 3.3 | [2.6-4.7] |
| Ketones | Neg | 57.9 | 2.3 | 4.0 | [3.1-5.6] |
| Ascorbic Acid | 20-40 mg/dL | 56.2 | 1.2 | 2.1 | [1.6-3.0] |
| Protein | Neg | 61.9 | 1.2 | 1.9 | [1.5-2.7] |
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| Analyte | Control Range | % Reflectance | Standard deviation | % CV | 95% Confidence Interval for CV |
| --- | --- | --- | --- | --- | --- |
| PH | 5-9 | 66.6 | 1.0 | 1.5 | [1.2-2.1] |
| Nitrite | Neg | 65.1 | 2.1 | 3.3 | [2.5-4.6] |
| Leukocyte | Neg | 62.5 | 1.9 | 3.0 | [2.4-4.3] |
| Specific Gravity | 1.000-1.035 | 27.9 | 1.3 | 4.5 | [3.6-6.6] |
| Blood | Neg | 65.4 | 0.8 | 1.2 | [0.9-1.7] |
| Glucose | Neg | 78.9 | 1.4 | 1.8 | [1.4-2.5] |
Total Precision, Candidate Device at Abnormal Levels
| Analyte | Control Range | % Reflectance | Standard deviation | % CV | 95% Confidence Interval for CV |
| --- | --- | --- | --- | --- | --- |
| Bilirubin | 1-4 mg/dL | 28.5 | 2.5 | 8.8 | [6.8-12.5] |
| Urobilinogen | 2-12 mg/dL | 38.2 | 1.9 | 5.0 | [3.8-7.0] |
| Ketones | 25-300 mg/dL | 7.5 | 0.7 | 9.3 | [7.2-13.3] |
| Ascorbic Acid | Neg | 7.9 | 0.7 | 8.9 | [6.8-12.6] |
| Protein | 30 - ≥500 mg/dL | 29.0 | 0.9 | 3.1 | [2.4-4.4] |
| PH | 5-9 | 39.8 | 1.3 | 3.3 | [2.5-4.6] |
| Nitrite | Pos | 46.4 | 1.5 | 3.2 | [2.5-4.6] |
| Leukocyte | 25-500 WBC’s/uL | 54.3 | 1.3 | 2.4 | [1.8-3.4] |
| Specific Gravity | 1.000-1.035 | 19.8 | 1.5 | 7.6 | [5.8-10.8] |
| Blood | 0.03-1 mg/dl | 4.1 | 0.1 | 2.4 | [1.9-3.4] |
| Glucose | 50-≥1000/mg/dL | 20.8 | 1.3 | 6.2 | [4.8-8.9] |
Within-Run Precision, Candidate Device at Normal Levels
| Analyte | Control Range | % Reflectance | Standard deviation | % CV | 95% Confidence Interval for CV |
| --- | --- | --- | --- | --- | --- |
| Bilirubin | Neg | 65.1 | 1.7 | 2.6 | [2.0-3.8] |
| Urobilinogen | Norm | 59.9 | 1.4 | 2.3 | [1.8-3.4] |
| Ketones | Neg | 60.6 | 1.5 | 2.5 | [1.9-3.6] |
| Ascorbic Acid | 20-40 | 55.4 | 0.7 | 1.3 | [1.0-1.8] |
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| Analyte | Control Range | % Reflectance | Standard deviation | % CV | 95% Confidence Interval for CV |
| --- | --- | --- | --- | --- | --- |
| | mg/dL | | | | |
| Protein | Neg | 60.7 | 0.4 | 0.66 | [0.5-1.0] |
| PH | 5-9 | 65.4 | 1.0 | 1.5 | [1.2-2.2] |
| Nitrite | Neg | 67.8 | 1.8 | 2.6 | [2.0-3.9] |
| Leukocyte | Neg | 64.8 | 1.2 | 1.8 | [1.4-2.7] |
| Specific Gravity | 1.000-1.035 | 26.1 | 0.8 | 3.1 | [2.3-4.5] |
| Blood | Neg | 65.0 | 0.6 | 0.9 | [0.7-1.3] |
| Glucose | Neg | 79.0 | 1.4 | 1.8 | [1.3-2.6] |
Within-Run Precision, Candidate Device at Abnormal Levels
| Analyte | Control Range | % Reflectance | Standard deviation | % CV | 95% Confidence Interval for CV |
| --- | --- | --- | --- | --- | --- |
| Bilirubin | 1-4 mg/dL | 30.5 | 1.7 | 5.6 | [4.2-8.2] |
| Urobilinogen | 2-12 mg/dL | 40.2 | 1.8 | 4.5 | [3.4-6.5] |
| Ketones | 25-300 mg/dL | 8.8 | 0.5 | 5.7 | [4.3-8.3] |
| Ascorbic Acid | Neg | 8.1 | 0.3 | 3.7 | [2.8-5.4] |
| Protein | 30 - ≥500 mg/dL | 29.0 | 0.4 | 1.4 | [1.0-2.0] |
| PH | 5-9 | 38.6 | 1.2 | 3.1 | [2.4-4.5] |
| Nitrite | Pos | 47.9 | 1.2 | 2.5 | [1.9-3.7] |
| Leukocyte | 25-500 WBC’s/uL | 55.5 | 0.8 | 1.4 | [1.1-2.1] |
| Specific Gravity | 1.000-1.035 | 21.1 | 1.4 | 6.6 | [5.0-9.7] |
| Blood | 0.03—1 mg/dl | 3.8 | 0.1 | 2.6 | [2.0-3.8] |
| Glucose | 50-≥1000/mg/dL | 20.1 | 1.5 | 7.4 | [5.7-10.9] |
# c. Linearity/assay reportable range:
Analyte free urine was fortified with glucose or hemoglobin to various concentrations spanning the reportable range of the assay. Results appear linear.
The discrete reporting levels for glucose are 0, 50, 150, 500, and $\geq 1000\mathrm{mg / dL}$ of glucose. Glucose Linearity Study Results
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| Known Analyte Concentration (mg/dL) | Observed Result (mg/dL) |
| --- | --- |
| 1500 | ≥ 1000 |
| 1000 | ≥ 1000 |
| 500 | 500 |
| 400 | 500 |
| 200 | 150 |
| 100 | 150 |
| 50 | 50 |
| 30 | 50 |
| 0 | 0 |
Discrete reporting levels for blood are 0, 0.03, 0.2, and 1.0 mg/dL hemoglobin.
Hemoglobin Linearity Study Results
| Known Analyte Concentration (mg/dL) | Observed Result |
| --- | --- |
| 1.0 | 1.0 |
| 0.9 | 1.0 |
| 0.5 | 1.0 |
| 0.1 | 0.2 |
| 0.03 | 0.03 |
| 0 | 0 |
See the method comparison section above for performance of spiked samples with the other analytes.
d. Carryover:
There are no apparent carryover issues with this test system/ instrument. Strips are unitized devices. They are manually dipped, with excess liquid being removed. Strips are then placed in a horizontal position for analysis. There is little chance for run over, and no problems were observed during studies.
e. Interfering Substances:
Because test strips have not been changed, and only minor modifications have been made to the instrument which should not affect performance, these studies were not repeated.
2. Other Supportive Instrument Performance Data Not Covered Above:
{14}
Instrument maintenance is minimal, i.e., wiping the outside of the instrument and transporter belt, and emptying the waste container.
**K. Proposed Labeling:**
The labeling is sufficient and satisfies the requirements of 21 CFR Part 809.10.
**L. Conclusion:**
The submitted information in this premarket notification is complete and supports a substantial equivalence decision.
15
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.