The BC-3200 auto hematology analyzer is a quantitative, automated hematology analyzer and leukocyte differential counter to be used in clinical laboratories for In Vitro Diagnostic purpose. The intended use of BC-3200 Auto Hematology Analyzer is to identify the normal patient, with all normal system-generated parameters, and to flag or identify patient results that require additional studies.
Device Story
The BC-3200 Auto Hematology Analyzer is an automated, quantitative hematology analyzer and leukocyte differential counter for clinical laboratory use. It processes human whole blood samples using two primary measurement methods: the Coulter method for WBC, RBC, and PLT counts, and a colorimetric method for HGB concentration. The system utilizes an automatic sampling, diluting, and mixing device. It outputs 16 hematological parameters and three histograms. The device flags results that fall outside of user-defined normal ranges, alerting clinicians to the need for further diagnostic investigation. It is operated by trained medical professionals. The system includes the analyzer, specific reagents, controls, and calibrators. By automating the counting and flagging process, the device assists in the efficient screening of patient blood samples, helping to identify those requiring manual review or additional testing.
Clinical Evidence
Clinical validation included a study of 200 samples comparing BC-3200 results to manual differential counts to assess flagging performance (82.5% agreement, 10.6% FPR, 45% FNR). Reproducibility was assessed via replicate testing (n=11) across low, normal, and high concentrations, showing CVs generally <5%. Linearity was verified using diluted samples across the analytical range. Inter-laboratory precision was evaluated using two sites. A normal range study was conducted on 121 donors to establish reference intervals. Correlation studies against the predicate device (n=103) showed high correlation coefficients (e.g., WBC r=0.9994, RBC r=0.9971).
Technological Characteristics
Automated hematology analyzer; impedance sensing for cell counting/sizing; colorimetric sensing for HGB. Dimensions/form factor: benchtop. Connectivity: barcode/manual keyboard entry. Software: moderate level of concern. Calibration: manual and auto-calibration programs. QC: L-J and X-B analysis programs. Sterilization: N/A (in vitro diagnostic instrument).
Indications for Use
Indicated for use in clinical laboratories by trained medical professionals to perform quantitative automated hematology analysis and leukocyte differential counting on human blood samples to identify normal patients and flag results requiring further investigation.
Regulatory Classification
Identification
An automated differential cell counter is a device used to identify one or more of the formed elements of the blood. The device may also have the capability to flag, count, or classify immature or abnormal hematopoietic cells of the blood, bone marrow, or other body fluids. These devices may combine an electronic particle counting method, optical method, or a flow cytometric method utilizing monoclonal CD (cluster designation) markers. The device includes accessory CD markers.
Special Controls
*Classification.* Class II (special controls). The special control for this device is the FDA document entitled “Class II Special Controls Guidance Document: Premarket Notifications for Automated Differential Cell Counters for Immature or Abnormal Blood Cells; Final Guidance for Industry and FDA.”
Predicate Devices
COULTER® ACT diff 2™ Analyzer (K0990352)
Submission Summary (Full Text)
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# 510(k) SUBSTANTIAL EQUIVALENCE DETERMINATION DECISION SUMMARY INSTRUMENT ONLY TEMPLATE
A. 510(k) Number:
K063407
B. Purpose for Submission:
This is an original-traditional 510(k) for a 16 parameter automated hematology analyzer and leukocyte differential cell counter with reagents, calibrator, and controls.
C. Manufacturer and Instrument Name:
Shenzhen Mindray Bio-Medical Electronics Co. LTD, BC-3200 Auto Hematology Analyzer
D. Type of Test or Tests Performed:
WBC, Lymph #, Mid#, Gran#, Lymph%, Mid%, Gran%, RBC, HGB, MCV, MCH, MCHC, RDW, HCT, MPV, and WBC Histogram, RBC Histogram, and PLT Histogram
E. System Descriptions:
1. Device Description:
The BC-3200 Auto Hematology Analyzer is a quantitative, automated hematology analyzer and leukocyte differential cell counter for In Vitro Diagnostic use in clinical laboratories. It is only to be used by trained medical professionals to identify the normal patient, with all normal system-generated parameters, and toflag or identify patient results that require additional studies. The analyzer provides analysis results of 16 parameter of human blood and three histograms.
2. Principles of Operation:
WBCs are counted and sized by the impedance method. This method is based on the measurement of changes in electrical resistance produced by a particle, which in this case is a blood cell suspended in a conductive diluent as it passes through an aperture of known dimensions. HGB is determined by the colorimetric method. RBCs and PLTs are counted by the impedance method also. In addition, for RBCs and Plts, volumetric metering is used. An accurate cell count cannot be obtained unless the precise volume of diluted sample that passes through the aperture during the count cycle is known. The analyzer uses a volumetric metering unit to
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control the count cycle and to ensure that a precise volume of sample is analyzed for the measurement.
3. **Modes of Operation:**
Closed Vial Whole Blood Mode, Whole Blood Mode for venous blood, and Predilute Mode for capillary blood.
4. **Specimen Identification:**
Barcode or manual keyboard entry.
5. **Specimen Sampling and Handling:**
Samples are manually loaded into a sample compartment one at a time. The BC-3200 utilizes an automatic sampling and mixing device for sample processing. The Mindray calibrator is called SC-CAL PLUS.
6. **Calibration:**
The device has two calibration programs: manual calibration and auto calibration using commercial calibrators. The Mindray calibrator is called SC-CAL PLUS.
7. **Quality Control:**
The device has two QC programs: L-J Analysis and X-B Analysis. The Mindray three level control is called BC-3D.
8. **Software:**
FDA has reviewed applicant’s Hazard Analysis and Software Development processes for this line of product types:
Yes ☐ X ☐ or No ☐ Mindray has provided software documentation at a moderate level of concern that conforms to the FDA software guidance document.
F. **Regulatory Information:**
1. **Regulation section:**
21 CFR 864.5220, Automated differential cell counter
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2. Classification:
Class II
3 Product code:
GKZ
4. Panel:
Hematology (81)
## G. Intended Use:
1. Indication(s) for Use:
The BC-3200 Auto Hematology Analyzer is a quantitative, automated hematology analyzer and leukocyte differential cell counter to be used in clinical laboratories for In Vitro Diagnostic Use. The intended use of the BC-3200 Auto Hematology analyzer is to identify the normal patient, with all normal system-generated parameters, and to flag or identify patient results that require additional studies.
2. Special Conditions for Use Statement(s):
N/A
## H. Substantial Equivalence Information:
1. Predicate Device Name(s) and 510(k) numbers:
COULTER® ACT diff 2™ Analyzer, K0990352
2. Comparison with Predicate Device:
| Similarities | | |
| --- | --- | --- |
| Item | Device | Predicate |
| Intended Use | The BC-3200 auto hematology analyzer is a quantitative, automated hematology analyzer and leukocyte differential counter for In Vitro Diagnostic Use in clinical laboratories. | Same |
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| Similarities | | |
| --- | --- | --- |
| Item | Device | Predicate |
| Sample Types | Whole Blood Mode and Prediluted Mode | Same |
| Sample Processing | Utilizes an automatic sampling, diluting, and mixing device for sample processing. | Same |
| Calibration | Provides 2 calibration programs: manual calibration and auto calibration using commercial calibrators. | Same |
| Aperture Alert | Minimize the possibility of reporting erroneous results caused by a partial or transient aperture clog or by other aperture disturbance. | Same |
| Differences | | |
| --- | --- | --- |
| Item | Device | Predicate |
| Operating Modes | Closed Vial Whole Blood Mode | Closed Vial Whole Blood Mode and Open Vial Whole Blood Mode |
| Throughput | 1 minute/analysis | 60 seconds or les |
| Quality Control | Provides 2 QC programs: L-J Analysis and X-B Analysis | Provides 1 QC program: L-J Analysis |
| Recommended Controls | BC-3D: Low, Normal, & High | 4C PLUS: Abnormal Low, Normal, Abnormal High |
| Sample Volume Aspirated | 13μL whole blood 20μL prediluted blood | 18μL whole blood 20μl prediluted blood |
| Parameters | WBC, RBC, HGB, HCT, MCV, MCH, MCHC, PLT, Lymph%, Lymph#, Mid%, Mid#, Gran%, Gran#, RDW, MPV | WBC, RBC, HGB, HCT, MCV, MCH, MCHC, PLT, Ly%, Ly#, MO%, MO#, GR%, GR#, RDW, MPV |
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I. Special Control/Guidance Document Referenced (if applicable):
Class II Special Controls Guidance Document: Premarket Notifications for Automated Differential Cell Counters for Immature or Abnormal Blood Cells; Final Guidance for Industry and FDA. December 4, 2001
J. Performance Characteristics:
1. Analytical Performance:
a. Accuracy:
Correlation is determined by comparing the results (both CBC and DIFF) obtained by the BC-3200 to those by the Coulter AC-T diff $2^{\mathrm{TM}}$ and by comparing the DIFF results obtained by the BC-3200 to those by manual differential.
Table 1: Correlation to Coulter $\mathbf{A}^{\mathrm{C}}$-T diff $2^{\mathrm{TM}}$
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Table 2: Correlation to Manual Differential
| Parameter | Samples (n) | Mean | | Difference ratio (D%) | Slope (a) | Intercept (b) | Correlation coefficient (r) |
| --- | --- | --- | --- | --- | --- | --- | --- |
| | | BC-3200 | A^{e}T diff 2 | | | | |
| WBC | 103 | 10.4 | 10.3 | 2.4 | 1.0097 | -0.0282 | 0.9994 |
| Lymph# | 98 | 1.9 | 2.1 | 11.8 | 0.9918 | -0.1864 | 0.9890 |
| Mid# | 98 | 0.7 | 0.5 | 40.5 | 2.1022 | -0.3798 | 0.9187 |
| Gran# | 98 | 6.1 | 6.0 | 3.7 | 0.9886 | 0.146 | 0.9978 |
| Lymph% | 98 | 25.8 | 29.3 | 11.5 | 0.7935 | 2.5772 | 0.9751 |
| Mid% | 98 | 9.0 | 6.7 | 43.0 | 0.7569 | 3.8798 | 0.4644 |
| Gran% | 98 | 65.2 | 64.0 | 3.4 | 0.9046 | 7.347 | 0.9707 |
| RBC | 103 | 4.31 | 4.27 | 1.7 | 0.9916 | 0.0702 | 0.9971 |
| HGB | 103 | 12.6 | 12.5 | 1.2 | 0.9951 | 0.0853 | 0.9982 |
| HCT | 103 | 37.6 | 37.2 | 2.2 | 1.0041 | 0.2953 | 0.9950 |
| MCV | 103 | 87.8 | 87.5 | 1.2 | 0.9549 | 4.3174 | 0.9824 |
| MCH | 103 | 29.2 | 29.5 | 1.6 | 0.9426 | 1.4345 | 0.9791 |
| MCHC | 103 | 33.3 | 33.7 | 1.8 | 0.7759 | 7.1720 | 0.6784 |
| RDW | 103 | 13.1 | 13.5 | 4.7 | 0.4393 | 7.1667 | 0.9569 |
| PLT | 103 | 226 | 230 | 8.0 | 0.8882 | 21.837 | 0.9961 |
| MPV | 102 | 8.5 | 8.9 | 4.7 | 0.7037 | 2.2287 | 0.9334 |
| Parameter | Samples (n) | Mean | | Slope | Intercept | Correlation Coefficient r |
| --- | --- | --- | --- | --- | --- | --- |
| | | BC-3200 | Manual differential | | | |
| Lymph% | 196 | 26.8 | 30.4 | 0.7575 | 3.7958 | 0.95 |
| Mid% | 196 | 9.2 | 9.0 | 0.3739 | 5.822 | 0.57 |
| Gran% | 196 | 64.0 | 60.6 | 0.8456 | 12.721 | 0.94 |
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# b. Precision/Reproducibility:
Reproducibility is stated in terms of both Standard Deviation (SD) and Coefficient of Variation (CV%). Reproducibility was determined by replicate testing $(n = 11)$ with samples of low, normal and high concentrations, three samples for each concentration. For each sample, results of the 2nd to 11th runs were adopted to calculate the SD and CV%.
Table 3: Imprecision, Low Concentration Samples
| 1 | WBC ×103/ μL | RBC ×106/μL | HGB (g/dL) | MCV (fl) | PLT ×103/μL |
| --- | --- | --- | --- | --- | --- |
| mean | 4.1 | 2.88 | 9.2 | 64.6 | 162 |
| SD | 0.07 | 0.04 | 0.1 | 0.40 | 5.06 |
| CV(%) | 1.63 | 1.45 | 0.8 | 0.62 | 3.12 |
| 2 | WBC ×103/ μL | RBC ×106/μL | HGB (g/dL) | MCV (fl) | PLT ×103/μL |
| mean | 3.2 | 3.02 | 9.3 | 72.9 | 155 |
| SD | 0.03 | 0.03 | 0.1 | 0.21 | 7.02 |
| CV(%) | 0.99 | 1.06 | 1.0 | 0.28 | 4.53 |
| 3 | WBC ×103/ μL | RBC ×106/μL | HGB (g/dL) | MCV (fl) | PLT ×103/μL |
| mean | 3.1 | 1.91 | 5.6 | 61.0 | 61 |
| SD | 0.06 | 0.03 | 0.1 | 0.24 | 5.11 |
| CV(%) | 1.84 | 1.76 | 1.1 | 0.39 | 8.39 |
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Table 4: Imprecision, Normal Concentration Samples
| 1 | WBC ×103/μL | RBC ×106/μL | HGB (g/dL) | MCV (fl) | PLT ×103/μL |
| --- | --- | --- | --- | --- | --- |
| mean | 10.1 | 4.60 | 13.1 | 83.3 | 244 |
| SD | 0.12 | 0.03 | 0.09 | 0.38 | 8.05 |
| CV(%) | 1.18 | 0.73 | 0.7 | 0.45 | 3.30 |
| 2 | WBC ×103/μL | RBC ×106/μL | HGB (g/dL) | MCV (fl) | PLT ×103/μL |
| mean | 9.8 | 5.34 | 15.2 | 83.1 | 249 |
| SD | 0.10 | 0.04 | 0.12 | 0.27 | 4.86 |
| CV(%) | 0.99 | 0.78 | 0.8 | 0.33 | 1.95 |
| 3 | WBC ×103/μL | RBC ×106/μL | HGB (g/dL) | MCV (fl) | PLT ×103/μL |
| mean | 11.3 | 5.27 | 15.0 | 85.9 | 231 |
| SD | 0.13 | 0.04 | 0.06 | 0.21 | 8.53 |
| CV(%) | 1.11 | 0.73 | 0.4 | 0.25 | 3.70 |
Table 5: Imprecision, Normal Concentration Samples
| 1 | WBC ×103/μL | RBC ×106/μL | HGB (g/dL) | MCV (fl) | PLT ×103/μL |
| --- | --- | --- | --- | --- | --- |
| mean | 10.1 | 4.60 | 13.1 | 83.3 | 244 |
| SD | 0.12 | 0.03 | 0.09 | 0.38 | 8.05 |
| CV(%) | 1.18 | 0.73 | 0.7 | 0.45 | 3.30 |
| 2 | WBC ×103/μL | RBC ×106/μL | HGB (g/dL) | MCV (fl) | PLT ×103/μL |
| mean | 9.8 | 5.34 | 15.2 | 83.1 | 249 |
| SD | 0.10 | 0.04 | 0.12 | 0.27 | 4.86 |
| CV(%) | 0.99 | 0.78 | 0.8 | 0.33 | 1.95 |
| 3 | WBC ×103/μL | RBC ×106/μL | HGB (g/dL) | MCV (fl) | PLT ×103/μL |
| mean | 11.3 | 5.27 | 15.0 | 85.9 | 231 |
| SD | 0.13 | 0.04 | 0.06 | 0.21 | 8.53 |
| CV(%) | 1.11 | 0.73 | 0.4 | 0.25 | 3.70 |
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# Inter-laboratory Precision:
Two laboratories, each having one BC-3200 installed, were selected for the test. Three samples of various concentrations (respectively low, normal and high) were prepared, each with sufficient volume to run twice on both of the BC-3200s. Each BC-3200 was operated by one operator, who conducted the test from beginning to the end. Each sample was divided into two aliquots, and the two aliquots were analyzed respectively by the two selected laboratories within the same day of preparation. Each aliquot was run twice on the BC-3200 and both runs were conducted within a short interval. No outlier was found during the test. Based on the data acquired, repeatability variance $(\mathrm{S}^{t2})$ , between laboratory variance $(\mathrm{S}^{j2})$ , and reproducibility variance $(\mathrm{S}_{\mathrm{R}}^2)$ of the following parameters, WBC, RBC, PLT, HGB, Lymph%, Mid% and Gran%, were calculated for ea concentration.
Table 6: Within-run Precision and Total Precision
| | | Low | Normal | High |
| --- | --- | --- | --- | --- |
| WBC×103/ μL | | | | |
| Mean | | 2.13 | 8.10 | 20.68 |
| Repeatability variance | S2r | 0.0025 | 0.0098 | 0.0613 |
| Between Laboratory variance | SL2 | 0.0000 | 0.0151 | 0.0000 |
| Reproducibility variance | SR2 | 0.0025 | 0.0249 | 0.0613 |
| | SR | 0.0500 | 0.1578 | 0.2476 |
| | CV% | 2.35% | 1.95% | 1.20% |
| Gran(%) | | | | |
| Mean | | 32.53 | 60.98 | 81.30 |
| Repeatability variance | S2r | 1.1050 | 0.0221 | 0.0637 |
| Between Laboratory variance | SL2 | 1.7588 | 0.7703 | 0.0932 |
| Reproducibility variance | SR2 | 2.8638 | 0.7924 | 0.1569 |
| | SR | 1.6923 | 0.8902 | 0.3961 |
| | CV% | 5.20% | 1.46% | 0.49% |
| Lymph (%) | | | | |
| Mean | | 12.65 | 28.83 | 51.30 |
| Repeatability variance | S2r | 0.2073 | 0.0613 | 3.0439 |
| Between Laboratory variance | SL2 | 0.0000 | 1.3307 | 6.4781 |
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| Reproducibility variance | SR2 | 0.2073 | 1.3920 | 9.5220 |
| --- | --- | --- | --- | --- |
| | SR | 0.4553 | 1.1798 | 3.0858 |
| | CV% | 3.60% | 4.09% | 6.02% |
| Mid (%) | | | | |
| Mean | | 6.05 | 10.20 | 16.18 |
| Repeatability variance | Sr2 | 0.0490 | 0.0098 | 0.6655 |
| Between Laboratory variance | SL2 | 0.0205 | 0.0751 | 1.3786 |
| Reproducibility variance | SR2 | 0.0695 | 0.0849 | 2.0441 |
| | SR | 0.2636 | 0.2914 | 1.4297 |
| | CV% | 4.36% | 2.86% | 8.84% |
| RBC (×106/μL) | | | | |
| Mean | | 2.48 | 4.89 | 5.80 |
| Repeatability variance | Sr2 | 0.0004 | 0.0065 | 0.0085 |
| Between Laboratory variance | SL2 | 0.0007 | 0.0013 | 0.0000 |
| Reproducibility variance | SR2 | 0.0011 | 0.0078 | 0.0085 |
| | SR | 0.0332 | 0.0883 | 0.0922 |
| | CV% | 1.34% | 1.81% | 1.59% |
| HGB (g/L) | | | | |
| Mean | | 6.35 | 14.08 | 19.13 |
| Repeatability variance | Sr2 | 0.0000 | 0.0025 | 0.0123 |
| Between Laboratory variance | SL2 | 0.0050 | 0.0601 | 0.0952 |
| Reproducibility variance | SR2 | 0.0050 | 0.0626 | 0.1075 |
| | SR | 0.0707 | 0.2502 | 0.3279 |
| | CV% | 1.11% | 1.78% | 1.71% |
| MCV (fl) | | | | |
| Mean | | 77.28 | 86.73 | 96.33 |
| Repeatability variance | Sr2 | 0.1103 | 0.0123 | 0.0907 |
| Between Laboratory variance | SL2 | 2.2562 | 1.5252 | 2.7160 |
| Reproducibility variance | SR2 | 2.3665 | 1.5375 | 2.8067 |
| | SR | 1.5383 | 1.2400 | 1.6753 |
| | CV% | 1.99% | 1.43% | 1.74% |
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Table 6 Appendix
| WBC | Form A | | | Form B | | | Form C | | |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| Laboratory | Low | Normal | High | Low | Normal | High | Low | Normal | High |
| 1 | 2.2 | 8.1 | 20.5 | 2.15 | 8.2 | 20.75 | 0.07 | 0.14 | 0.35 |
| | 2.1 | 8.3 | 21 | | | | | | |
| 2 | 2.1 | 8 | 20.6 | 2.1 | 8 | 20.6 | 0 | 0 | 0 |
| | 2.1 | 8 | 20.6 | | | | | | |
| Gran(%) | Form A | | | Form B | | | Form C | | |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| Laboratory | Low | Normal | High | Low | Normal | High | Low | Normal | High |
| 1 | 32.5 | 60.2 | 81.1 | 31.45 | 60.35 | 81.05 | 1.48 | 0.21 | 0.07 |
| | 30.4 | 60.5 | 81 | | | | | | |
| 2 | 33.5 | 61.6 | 81.8 | 33.6 | 61.6 | 81.55 | 0.14 | 0 | 0.35 |
| | 33.7 | 61.6 | 81.3 | | | | | | |
| Lymph (%) | Form A | | | Form B | | | Form C | | |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| Laboratory | Low | Normal | High | Low | Normal | High | Low | Normal | High |
| 1 | 12.8 | 29.9 | 51.7 | 12.75 | 29.65 | 53.3 | 0.07 | 0.35 | 2.26 |
| | 12.7 | 29.4 | 54.9 | | | | | | |
| 2 | 12.1 | 28 | 50 | 12.55 | 28 | 49.3 | 0.64 | 0 | 0.99 |
| | 13 | 28 | 48.6 | | | | | | |
| Mid (%) | Form A | | | Form B | | | Form C | | |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| Laboratory | Low | Normal | High | Low | Normal | High | Low | Normal | High |
| 1 | 6.1 | 9.9 | 15.8 | 6.2 | 10 | 15.25 | 0.14 | 0.14 | 0.78 |
| | 6.3 | 10.1 | 14.7 | | | | | | |
| 2 | 6.1 | 10.4 | 16.5 | 5.9 | 10.4 | 17.1 | 0.28 | 0 | 0.85 |
| | 5.7 | 10.4 | 17.7 | | | | | | |
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| RBC | Form A | | | Form B | | | Form C | | |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| Laboratory | Low | Normal | High | Low | Normal | High | Low | Normal | High |
| 1 | 2.44 | 4.78 | 5.84 | 2.455 | 4.845 | 5.765 | 0.02 | 0.09 | 0.11 |
| | 2.47 | 4.91 | 5.69 | | | | | | |
| 2 | 2.51 | 4.99 | 5.89 | 2.495 | 4.94 | 5.84 | 0.02 | 0.07 | 0.07 |
| | 2.48 | 4.89 | 5.79 | | | | | | |
| HGB | Form A | | | Form B | | | Form C | | |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| Laboratory | Low | Normal | High | Low | Normal | High | Low | Normal | High |
| 1 | 6.3 | 13.9 | 18.8 | 6.3 | 13.9 | 18.9 | 0 | 0 | 0.14 |
| | 6.3 | 13.9 | 19 | | | | | | |
| 2 | 6.4 | 14.3 | 19.4 | 6.4 | 14.25 | 19.35 | 0 | 0.07 | 0.07 |
| | 6.4 | 14.2 | 19.3 | | | | | | |
| MCV | Form A | Form B | Form C | | | | | | |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| Laboratory | Low | Normal | High | Low | Normal | High | Low | Normal | High |
| 1 | 76.5 | 85.9 | 95.2 | 76.2 | 85.85 | 95.15 | 0.42 | 0.07 | 0.07 |
| | 75.9 | 85.8 | 95.1 | | | | | | |
| 2 | 78.5 | 87.5 | 97.8 | 78.35 | 87.6 | 97.5 | 0.21 | 0.14 | 0.42 |
| | 78.2 | 87.7 | 97.2 | | | | | | |
| PLT | Form A | | | Form B | | | Form C | | |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| Laboratory | Low | Normal | High | Low | Normal | High | Low | Normal | High |
| 1 | 88 | 265 | 466 | 91.5 | 264.5 | 462.5 | 4.95 | 0.71 | 4.95 |
| | 95 | 264 | 459 | | | | | | |
| 2 | 97 | 248 | 474 | 98 | 252 | 474.5 | 1.41 | 5.66 | 0.71 |
| | 99 | 256 | 475 | | | | | | |
# c. Linearity:
Linearity was determined by running diluted samples. RBC,HGB are diluted by blood plasma of the sample, while WBC and PLT are diluted by specified diluent. Concentrations from 0 to $100\%$ were tested, each concentration twice. The average of the two runs is taken as the result, together with the concentration, to calculate per the linear regression equation.
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Table 7: WBC Linearity
| Dilution% | Test 1 | Test 2 | Mean | Ideal | Error | Proportional Error |
| --- | --- | --- | --- | --- | --- | --- |
| 100 | 117.1 | 115.9 | 116.50 | 120.01 | 3.51 | 2.9 |
| 80 | 99.8 | 100.1 | 99.95 | 96.01 | -3.94 | -4.1 |
| 60 | 73.4 | 72.1 | 72.75 | 72.00 | -0.75 | -1.0 |
| 40 | 47.8 | 48.6 | 48.20 | 48.00 | -0.20 | -0.4 |
| 20 | 23.1 | 23.1 | 23.10 | 23.99 | 0.89 | 3.7 |
| 10 | 12.1 | 12.0 | 12.05 | 11.99 | -0.06 | -0.5 |
| 5 | 6.0 | 6.2 | 6.10 | 6.00 | -0.10 | -1.7 |
| 2.5 | 3.0 | 2.9 | 2.95 | 2.99 | 0.04 | 1.3 |
| 1.25 | 1.3 | 1.3 | 1.30 | 1.49 | 0.19 | 12.8 |
| 0.625 | 0.5 | 0.5 | 0.50 | 0.74 | 0.24 | 32.4 |
| 0.3125 | 0.2 | 0.1 | 0.15 | 0.36 | 0.21 | 58.3 |
| 0 | 0 | 0 | 0.00 | -0.01 | -0.01 | / |
| Slope | 1.2002 | | | | | |
| Intercept | -0.0129 | | | | | |
Table 8: RBC Linearity
| Dilution% | Test 1 | Test 2 | Mean | Ideal | Error | Proportional Error |
| --- | --- | --- | --- | --- | --- | --- |
| 100 | 8.46 | 8.43 | 8.445 | 8.519 | 0.074 | 0.9 |
| 80 | 6.91 | 6.86 | 6.885 | 6.819 | -0.066 | -1.0 |
| 60 | 5.12 | 5.17 | 5.145 | 5.119 | -0.026 | -0.5 |
| 40 | 3.42 | 3.46 | 3.440 | 3.419 | -0.021 | -0.6 |
| 20 | 1.71 | 1.69 | 1.700 | 1.719 | 0.019 | 1.1 |
| 10 | 0.89 | 0.87 | 0.880 | 0.869 | -0.011 | -1.3 |
| 5 | 0.46 | 0.46 | 0.460 | 0.444 | -0.016 | -3.6 |
| 2.5 | 0.21 | 0.22 | 0.215 | 0.232 | 0.017 | 7.3 |
| 1.25 | 0.10 | 0.13 | 0.115 | 0.125 | 0.010 | 8.0 |
| 0 | 0.00 | 0.00 | 0.000 | 0.019 | 0.019 | / |
| Slope | 0.0850 | | | | | |
| Intercept | 0.0191 | | | | | |
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Table 9: HGB Linearity
| Dilution% | Test 1 | Test 2 | Mean | Ideal | Error | Proportional Error |
| --- | --- | --- | --- | --- | --- | --- |
| 100 | 25.6 | 25.6 | 25.60 | 25.40 | -0.20 | -0.8 |
| 80 | 20.5 | 20.1 | 20.30 | 20.33 | 0.03 | 0.1 |
| 60 | 15.1 | 14.9 | 15.00 | 15.26 | 0.26 | 1.7 |
| 40 | 10.1 | 10.1 | 10.10 | 10.19 | 0.09 | 0.9 |
| 20 | 5.2 | 5.0 | 5.10 | 5.11 | 0.01 | 0.2 |
| 10 | 2.7 | 2.6 | 2.65 | 2.58 | -0.07 | -2.7 |
| 5 | 1.4 | 1.4 | 1.40 | 1.31 | -0.09 | -6.9 |
| 2.5 | 0.7 | 0.7 | 0.70 | 0.68 | -0.02 | -2.9 |
| 1.25 | 0.4 | 0.4 | 0.40 | 0.36 | -0.04 | -11.1 |
| 0 | 0.0 | 0.0 | 0.00 | 0.04 | 0.04 | / |
| Slope | 0.2536 | | | | | |
| Intercept | 0.0425 | | | | | |
Table 10: PLT Linearity
| Dilution% | Test 1 | Test 2 | Mean | Ideal | Error | Proportional Error |
| --- | --- | --- | --- | --- | --- | --- |
| 100 | 1014 | 1008 | 1011.0 | 1040.3 | 29.3 | 2.8 |
| 80 | 850 | 858 | 854.0 | 832.5 | -21.5 | -2.6 |
| 60 | 631 | 650 | 640.5 | 624.8 | -15.7 | -2.5 |
| 40 | 425 | 419 | 422.0 | 417.0 | -5.0 | -1.2 |
| 20 | 221 | 208 | 214.5 | 209.3 | -5.2 | -2.5 |
| 10 | 109 | 101 | 105.0 | 105.4 | 0.4 | 0.4 |
| 5 | 53 | 53 | 53.0 | 53.5 | 0.5 | 0.9 |
| 2.5 | 23 | 17 | 20.0 | 27.5 | 7.5 | 27.3 |
| 1.25 | 8 | 5 | 6.5 | 14.5 | 8.0 | 55.2 |
| 0 | 0 | 0 | 0.0 | 1.6 | 1.6 | / |
| Slope | 10.3871 | | | | | |
| Intercept | 1.5618 | | | | | |
# d. Carryover:
Carryover was determined by first running the high concentration sample for three consecutive times (i1, i2, i3) and then the low concentration sample three consecutive times (j1, j2, j3), and finally calculating per the following equation: Carryover $(\%) = [(j1 - j3) / (i3 - j3)] \times 100\%$ . The test was then repeated using the high level control.
{14}
Table 11: Carryover, High Concentration Sample
| Parameter | High Concentration Sample (Whole Blood) | | | Low Concentration Sample (Whole Blood) | | | Carryover % |
| --- | --- | --- | --- | --- | --- | --- | --- |
| | i1 | i2 | i3 | j1 | j2 | j3 | |
| WBC(×103/μL) | 19.7 | 20.4 | 20.0 | 1.9 | 1.9 | 1.9 | 0% |
| RBC(×106/μL) | 6.34 | 6.24 | 6.2 | 1.87 | 1.96 | 1.85 | 0.46% |
| HGB(g/dL) | 25.4 | 25.0 | 24.8 | 3.3 | 3.2 | 3.2 | 0.46% |
| PLT(×103/μL) | 404 | 390 | 396 | 31 | 34 | 33 | 0% |
Table 12: Carryover, High Level Control
| Parameter | High Concentration Sample (High Level Control) | | | Low Concentration Sample (Specified Diluent) | | | Carryover % |
| --- | --- | --- | --- | --- | --- | --- | --- |
| | i1 | i2 | i3 | j1 | j2 | j3 | |
| WBC(×103/μL) | 21.7 | 21.3 | 21.7 | 0.0 | 0.0 | 0.0 | 0% |
| RBC(×106/μL) | 5.88 | 5.79 | 5.79 | 0.00 | 0.00 | 0.00 | 0% |
| HGB(g/dL) | 18.8 | 18.7 | 18.9 | 0.0 | 0.0 | 0.0 | 0% |
| PLT(×103/μL) | 453 | 438 | 429 | 0 | 0 | 0 | 0% |
e. Interfering Substances:
N/A
2. Other Supportive Instrument Performance Data Not Covered Above:
Ability to Flag Abnormal Histograms:
BC-3200's ability to flag abnormal WBC histograms was determined by comparing 200 sample results obtained by the BC-3200 to those obtained by manual differential.
Table 13: Ability to Flag Abnormal WBC Histograms
| Manual differential | BC-3200 | |
| --- | --- | --- |
| | Positive (39) | Negative (161) |
| Positive (40) | TP □22□ | FN □18□ |
| Negative (160) | FP □17□ | TN □143□ |
| Agreement% | False Positive Ratio (%) | False Negative Ratio (%) |
| 82.5 | 10.6 | 45 |
{15}
# Reference Ranges
A Normal Ranges Study was conducted to assess the Reference Ranges for the BC-3200 analyzer. Whole-blood samples were collected from 121 donors.
Table 14: Reference Ranges
| Parameter | Units | Sex | Mean | 90% Confidence Low Limit | 90% Confidence High Limit |
| --- | --- | --- | --- | --- | --- |
| WBC | ×103cells /μL | M/F | 6.86 | 3.47 | 10.25 |
| RBC | ×106cells /μL | M/F | 4.56 | 3.54 | 5.58 |
| Hgb | g/ dL | M/F | 13.40 | 10.27 | 16.52 |
| Hct | ratio | M/F | 40.12 | 30.98 | 49.26 |
| MCV | fL | M/F | 88.18 | 80.82 | 95.55 |
| MCH | pg | M/F | 29.36 | 26.57 | 32.15 |
| MCHC | g/ dL | M/F | 33.33 | 32.09 | 34.56 |
| Plt | ×103cells/μL | M/F | 209.92 | 119.62 | 300.22 |
| RDW | % | M/F | 12.81 | 11.53 | 14.10 |
| MPV | fL | M/F | 8.47 | 7.07 | 9.87 |
| LY | % | M/F | 27.33 | 18.11 | 36.55 |
| MO | % | M/F | 9.45 | 5.23 | 13.67 |
| GR | % | M/F | 63.26 | 51.62 | 74.89 |
# K. Proposed Labeling:
The labeling is sufficient and it 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.
{16}
17
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Learn the FDA Browser
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.