The BD FACSCanto System with BD FACSDiva software is intended for use as an In Vitro Diagnostic device for identification and enumeration of lymphocyte subsets in human cells in suspension using a lyse wash sample preparation method for flow cytometry.
Device Story
Flow cytometer system comprising cytometer, wet cart, and computer; acquires/analyzes lysed, washed whole blood samples. Fluidic, optic, and electronic subsystems measure light signals (size, shape, granularity, fluorescence) as particles pass through a glass cuvette. Digital electronics process signals; BD FACSDiva software facilitates instrument setup, data acquisition, and analysis. Used in clinical laboratories by trained personnel. Operator performs manual or automated sample introduction. Software enables gating for multicolor analysis and reporting of lymphocyte subset percentages. Output assists clinicians in immunophenotyping; provides quantitative data on cell populations.
Clinical Evidence
Bench testing only. Performance evaluated per NCCLS guidelines: EP9-A2 for accuracy and EP5-A for precision. Additional testing included system carryover and linearity per FDA guidance for Automated Differential Cell Counters. Results demonstrated comparable accuracy to the predicate, acceptable system precision, and acceptable carryover and linearity performance.
Indicated for immunophenotyping in clinical laboratories using cleared IVD flow cytometry assays with lyse wash preparation. Specifically for lymphocyte subsets: CD3+CD8+, CD3+CD4+, CD3-CD16+ and/or CD56+, CD3-CD19+, and CD3+.
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.”
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# 510(k) SUBSTANTIAL EQUIVALENCE DETERMINATION DECISION SUMMARY INSTRUMENT ONLY TEMPLATE
A. 510(k) Number:
K040725
B. Purpose for Submission:
New device
C. Manufacturer and Instrument Name:
Becton Dickinson Immunocytometry Systems
BD FACS Canto Flow Cytometer with BD FACS Diva Software
D. Type of Test or Tests performed:
Semi-quantitative, optical
E. System Descriptions:
1. Device Description:
The BD FACS Canto System is comprised of a flow cytometer, a wet cart, and a computer. The wet cart contains operational fluids, the flow cytometer acquires and analyzes the sample, and the computer displays and prints the analysis. The flow cytometer utilizes three subsystems: fluidics, optics and electronics. It contains one software package for manual immunophenotyping and is compatible with the BD FACS Loader for automatic sample introduction.
2. Principles of Operation:
The BD FACS Canto flow cytometer combines fluidic, optic, and electronic subsystems to measure and analyze signals emitted when particles flow in a liquid stream through a glass cuvette, at which beams of laser light are directed. The emitted light from these particles provides information about cell size, shape, granularity, and fluorescence intensity.
3. Modes of Operation:
Random access or automatic sampling, open tube
4. Specimen Identification:
Manual identification by operator or instrument automatic numbering
5. Specimen Sampling and Handling:
Lysed washed cell suspension from whole blood sample; open tube using manual or automated sample introduction.
6. Calibration:
Not provided.
7. Quality Control:
Instrument quality control is performed to ensure consistent instrument performance. Quality control parameters should be as constant as possible
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using the same particle (beads) type, lot number and flow rate from day to day. The BD FACS Diva software provides a template to use as a starting point for the QC experiment. The QC experiment contains a preformatted global worksheet. The worksheet contains the analysis objects (plots, gates, statistics) needed to perform QC.
8. Software:
BD FACSDiva software can be used to facilitate instrument setup, communicate between the cytometer and the computer, and acquire and analyze data. The software allows the setting of gates for multicolor data analysis, and to report lymphocyte subset percentage.
FDA has reviewed the applicant’s Hazard Analysis and software Documentation: Yes ☑ or No ☐
F. Regulatory Information:
1. Regulation Section: 21 CFR 864.5220, Automated differential cell counter
2. Classification: Class II
3. Product Code: GKZ, Counter, differential cell
4. Panel: Hematology (81)
G. Intended Use:
1. Indication(s) for Use:
BD FACS Canto Flow Cytometer with BD FACS Diva Software is used for immunophenotyping in clinical laboratories, using previously cleared IVD assays for flow cytometry that utilize the lyse wash sample preparation method. The lymphocytes subsets include; CD3⁺CD8⁺, CD3⁺CD4⁺, CD3⁻ CD16⁺ and/or CD56⁺, CD3⁻CD19⁺, and CD3⁺.
2. Special Condition for use Statement(s):
Not applicable.
H. Substantial Equivalence Information:
1. Predicate device name(s) and 510(k) numbers:
BD FACSCalibur, K973483
2. Comparison with Predicate Device:
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| Item | Device | Predicate |
| --- | --- | --- |
| | BD FACS Canto Flow Cytometer with FACS Diva Software | BD FACSCalibur with FACSComp Software |
| Intended Use | For use as an in-vitro diagnostic device for identification and enumeration of lymphocyte subsets in human cells in suspension using a lyse wash sample preparation method for flow cytometry. | Same |
| Lasers | Blue – 488 nm solid state
Red – 633 nm HeNe | Blue – 488 nm argon ion
Red – 635 nm diode laser |
| Software | BD FACSDiva version 4.0 | BD Simulset™ |
| Differences | | |
| Item | Device | Predicate |
| | | |
| Detectors | 1 FSC photodiode
1 SSC photomultiplier tube
4 fluorescence detector PMTs
plus 2 additional fluorescence detector PMTs | Same FSC
Same SSC
4 fluorescence detector PMTs |
| Optics | Laser light delivered by fiber optics, prisms and lasers
Emitted light delivered by collection an fiber optics | Laser light delivered by mirrors, prisms and lenses
Emitted light delivered by mirrors |
| Electronics | Digital | Analog |
| Fluidics | Addition of an external wet cart to supply bulk fluids and hold waste. | BD FACS Flow sheath fluid cubitainer |
I. Standard/Guidance Document Referenced (if applicable):
EP9-A2 Method Comparison and Bias Estimation Using Patient Samples, Approved Standard-Second Edition, NCCLS
EP5A Evaluation of Precision Performance of Clinical Chemistry Device Approved Guideline, NCCLS
Class II Special Controls Guidance Document: Premarket Notification for Automated Differential Cell Counters for immature or Abnormal Blood Cells; Final Guidance for Industry and FDA.
EP6-A Evaluation of the Linearity of Quantitative Measurement Procedures, Approved Guideline, NCCLS
ICSH Expert Panel on Cytometry, Guidelines for Evaluation of Blood Analyzers, Cytometry Clinical Laboratory Haematolog, 1994, 16, 157-
J. Performance Characteristics:
1. Analytical Performance:
a. Accuracy:
A comparison study was performed using 128 samples from both normal and abnormal donors from three donor sites. The reported
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subset percentages were compared to the predicate device. See linear regression results below:
| Measurement | Unit | CD3 | CD4 | CD8 | CD19 | CD16+56 |
| --- | --- | --- | --- | --- | --- | --- |
| Number | | 128 | 128 | 128 | 128 | 128 |
| Slope | | 0.99 | 1.03 | 0.98 | 1.01 | 0.96 |
| Confidence Interval | | 0.96,1.02 | 1.01,1.05 | 0.96,1.00 | 0.97,1.06 | 0.93,0.98 |
| Intercept | % | 1.9 | -0.50 | -99 | -0.20 | -0.85 |
| Confidence Interval | | -0.31, 4.11 | -0.03, 0.02 | -2.07,0.09 | -0.83,0.44 | -1.28,0.41 |
| Correlation Coefficient | % | 0.986 | 0.995 | 0.993 | 0.968 | 0.988 |
b. Precision/Reproducibility:
Two levels of control cells were run on three BD FACS Canto instruments equipped with a BD FACS Loader by four different operators. Measurements were obtained from two separate runs per day over 20 days; each runs separated by a minimum of four hours.
Precision Summary
| Lymphocyte subset | Within-Run Precision (SD) | Within-Run CV | Total Precision (SD) | Total CV |
| --- | --- | --- | --- | --- |
| Unit | % | % | % | % |
| CD3 | 1.04 | 1.6 | 1.17 | 1.8 |
| CD4 | 0.92 | 2.8 | 0.99 | 3.0 |
| CD8 | 1.06 | 3.5 | 1.15 | 3.8 |
| CD19 | 0.82 | 4.9 | 0.89 | 5.3 |
| CD16+56 | 0.79 | 4.8 | 0.83 | 5.1 |
c. Linearity:
Linearity was tested using beads with different fluorescence intensities. Five to seven intensities were tested for each of the six detectors. Ten replications were measured on each of three instruments. A summary of the results are as follows:
| Detector | Reference intensity range | Conclusion |
| --- | --- | --- |
| FITC (FL1) | 600-330,000 MEFL | Acceptable |
| PE (FL2) | 400-300,000 MEPE | Acceptable |
| PerCP-Cy5.5 (FL3) | 1,900-1,115,000 MEPCY5 | Acceptable |
| APC (FL4) | 0.025-100 RFI | Acceptable |
| PE-Cy7 (FL5) | 0.098-100 RFI | Acceptable |
| APC-Cy7 (FL6) | 0.025-100 RFI | Acceptable |
d. Carryover:
A carryover study was performed. Percent carryover was estimated by introducing three consecutive abnormally high leucocyte prepared
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samples followed by three consecutive abnormally low leucocyte prepared samples, and calculating the percent difference. All data collected with and without the BD FACS Loader met acceptable criteria.
e. Interfering Substances:
Not provided
2. Other Supportive Instrument Performance Data Not Covered Above
K. Conclusion:
The submitted information in this premarket notification is complete and supports a substantial equivalence decision.
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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.
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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?
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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.
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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.
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Reading rule for every project: how many summaries do you read in full?
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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.
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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.
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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.
