DEN130011 · Illumina, Inc. · PFF · Nov 19, 2013 · Immunology
Device Facts
Record ID
DEN130011
Device Name
ILLUMINA MISEQDX PLATFORM
Applicant
Illumina, Inc.
Product Code
PFF · Immunology
Decision Date
Nov 19, 2013
Decision
DENG
Submission Type
Post-NSE
Regulation
21 CFR 862.2265
Device Class
Class 2
Indications for Use
The MiSeqDx Platform is a sequencing instrument that measures fluorescence signals of labeled nucleotides through the use of instrument specific reagents and flow cells (MiSeqDx Universal Kit 1.0), imaging hardware, and data analysis software. The MiSeqDx Platform is intended for targeted sequencing of human genomic DNA from peripheral whole blood samples. The MiSeqDx Platform is not intended for whole genome or de novo sequencing.
Device Story
MiSeqDx Platform is a high-throughput genetic sequence analyzer; measures fluorescence signals of labeled nucleotides; utilizes instrument-specific reagents, flow cells, imaging hardware, and data analysis software. Used in clinical settings for targeted sequencing of human genomic DNA from peripheral whole blood. Instrument processes samples to generate nucleic acid sequence data; software performs sequence alignment and variant calling. Healthcare providers use output to identify specific sequence variations; aids in clinical decision-making regarding genetic analysis. System requires validation of specimen types, nucleic acid types, and sequence variation types (e.g., SNVs, insertions, deletions).
Clinical Evidence
No clinical trial data provided. Evidence based on analytical performance validation requirements including accuracy and reproducibility data using well-characterized sample panels, comparison to high-confidence reference sequence data, and assessment of interference and indexing primer performance.
Technological Characteristics
High-throughput genomic sequence analyzer; utilizes fluorescence signal measurement of labeled nucleotides. Components include imaging hardware, reagent handling systems, and data analysis software. Requires specific flow cells and reagents. Operates via targeted sequencing methodology. Subject to special controls regarding labeling, validation of read depth, nucleic acid extraction methods, and performance characteristics (accuracy/reproducibility).
Indications for Use
Indicated for targeted sequencing of human genomic DNA from peripheral whole blood samples for clinical use. Not indicated for whole genome or de novo sequencing.
Regulatory Classification
Identification
A high throughput genomic sequence analyzer for clinical use is an analytical instrument system intended to generate, measure and sort signals in order to analyze nucleic acid sequences in a clinical sample. The device may include a signal reader unit; reagent handling, dedicated instrument control, and other hardware components; raw data storage mechanisms; data acquisition software; and software to process detected signals.
Special Controls
*Classification.* Class II (special controls). The device is exempt from the premarket notification procedures in subpart E of part 807 of this chapter subject to the limitations in § 862.9. The special controls for this device are:(1) The labeling for the instrument system must reference legally marketed pre-analytical and analytical reagents to be used with the instrument system and include or reference legally marketed analytical software that includes sequence alignment and variant calling functions, to be used with the instrument system.
(2) The labeling for the instrument system must include a description of the following information:
(i) The specimen type(s) validated as an appropriate source of nucleic acid for this instrument.
(ii) The type(s) of nucleic acids (
*e.g.,* germline DNA, tumor DNA) validated with this instrument.(iii) The type(s) of sequence variations (
*e.g.* single nucleotide variants, insertions, deletions) validated with this instrument.(iv) The type(s) of sequencing (
*e.g.,* targeted sequencing) validated with this instrument.(v) The appropriate read depth for the sensitivity claimed and validation information supporting those claims.
(vi) The nucleic acid extraction method(s) validated for use with the instrument system.
(vii) Limitations must specify the types of sequence variations that the instrument cannot detect with the claimed accuracy and precision (
*e.g.,* insertions or deletions larger than a certain size, translocations).(viii) Performance characteristics of the instrument system must include:
(A) Reproducibility data generated using multiple instruments and multiple operators, and at multiple sites. Samples tested must include all claimed specimen types, nucleic acid types, sequence variation types, and types of sequencing. Variants queried shall be located in varying sequence context (
*e.g.,* different chromosomes, GC-rich regions). Device results shall be compared to reference sequence data with high confidence.(B) Accuracy data for all claimed specimen types and nucleic acid types generated by testing a panel of well characterized samples to query all claimed sequence variation types, types of sequencing, and sequences located in varying sequence context (
*e.g.,* different chromosomes, GC-rich regions). The well-characterized sample panel shall include samples from at least two sources that have highly confident sequence based on well-validated sequencing methods. At least one reference source shall have sequence generated independently of the manufacturer with respect to technology and analysis. Percent agreement and percent disagreement with the reference sequences must be described for all regions queried by the instrument.(C) If applicable, data describing endogenous or exogenous substances that may interfere with the instrument system.
(D) If applicable, data demonstrating the ability of the system to consistently generate an accurate result for a given sample across different indexing primer combinations.
(ix) The upper and lower limit of input nucleic acid that will achieve the claimed accuracy and reproducibility. Data supporting such claims must also be summarized.
Submission Summary (Full Text)
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# EVALUATION OF AUTOMATIC CLASS III DESIGNATION FOR MISEQDX PLATFORM
# DECISION SUMMARY INSTRUMENT ONLY TEMPLATE Correction Date: February 24, 2017
This Decision Summary contains corrections to the November 19, 2013 Decision Summary.
| A. 510(k) Number: | k123989 |
|-------------------------------------|-----------------------------------------------------------------------------------------------------|
| B. Purpose for Submission: | De novo request for evaluation of automatic class III designation for the Illumina MiSeqDx Platform |
| C. Type of Test or Tests Performed: | High-throughput DNA sequencing |
| D. Applicant: | Illumina Inc. |
| E. Device Name: | MiSeqDx Platform |
### F. Regulatory Information:
FDA identifies this type of device as:
- 1. New regulation number: 21 CFR 862.2265
- 2. Classification: Class II.
- 3. Product code: PFF High throughput DNA sequence analyzer
- 4. Panel: Toxicology (91)
# G. Intended Use:
- 1.Intended uses(s):
The MiSeqDx Platform is a sequencing instrument that measures fluorescence signals of labeled nucleotides through the use of instrument specific reagents and flow cells (MiSeqDx Universal Kit 1.0), imaging hardware, and data analysis software. The MiSeqDx Platform is intended for targeted sequencing of human genomic DNA from peripheral whole blood samples. The MiSeqDx Platform is not intended for whole genome or de novo sequencing.
- 2. Indication for uses(s):
Same as intended use above.
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## 3. Special conditions for use statement(s):
- 1. This product is limited to delivering:
- Sequencing output >1 Gb o
- Reads >3 million o
- o Read length (in paired end run) 2 x 150 bp
- Bases higher than 030 >75% (Greater than 75% of bases have Phred scale . quality score greater than 30, indicating base call accuracy greater than 99.9%)
- 2. Variants in homopolymer runs exceeding eight bases will be filtered out in the VCF files (R8 filter).
- The system has been validated for the detection of SNVs and up to 3 base deletions. 3. Evaluation of 1 base insertions was been limited to 3 different insertions on 3 separate chromosomes.
- The system has problems detecting 1 base insertions or deletions in homopolymer 4. tracts (e.g., polyA).
- 5. This MiSeqDx system is designed to deliver qualitative (i.e. genotype) results.
- 6. As with any hybridization-based workflow, underlying polymorphisms or mutations in oligonucleotide-binding regions can affect the alleles being probed and, consequently, the calls made.
- 7. Recommended minimal coverage per amplicon needed for accurate variant calling (Q(max gt | poly site) >= 100) is 75x.
# H. System Descriptions:
# 1. Device Description:
The MiSeqDx Platform is a high throughput DNA sequence analyzer for clinical use.
The MiSeqDx Platform consists of the MiSeqDx instrument and data analysis software. It is for use with the MiSeqDx Universal Kit 1.0 [MiSeqDx reagent cartridge, MiSeqDx flow cell, SBS Solution (PR2 buffer)] for library preparation and sample indexing (k133136). The end-user inputs extracted genomic DNA to be sequenced and provides the Analyte Specific Reagents (ASRs) to develop a sequencing assay that targets their sequence of interest.
# 2. Principles of Operation:
Testing begins with genomic DNA extracted from a peripheral whole blood sample. The genomic DNA is processed through library preparation. which specifically amplifies the intended genomic regions of each sample while also adding the indexes and flow cell capture sequences to the amplified products. The resulting sample libraries are then transferred into a MiSeqDx reagent cartridge which contains all of the reagents required for cluster generation and sequencing (Sequencing By Synthesis - SBS). The MiSeqDx cartridge, MiSeqDx flow cell, and MiSeqDx SBS Solution (PR2 buffer) are then inserted
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into the MiSeqDx instrument which performs cluster generation, sequencing and data analysis.
The instrument uses cluster generation on the flow cell surface followed by sequencing using the Sequencing by Synthesis (SBS) process.
After the flow cell images are captured by the MiSeqDx instrument following each sequencing cycle, primary analysis is performed without user intervention. Primary analysis is performed by the RTA (Real Time Analysis) software, and consists of base calling of each cluster at each cycle. In addition to calling the bases, RTA assigns an analytical quality score (O-score) to each base call. Calculations of O-scores are based on the ratio of the signal intensity of the highest base in a given cluster during a given cycle to the signal intensity of the three other bases. The quality score Q is calculated as -10 log10 P, where P is the probability that base call is incorrect.
Secondary analysis is performed by the MiSeqDx Reporter software. It also occurs without user intervention and consists of de-multiplexing and FASTO file generation. De-multiplexing is the process of using the index sequences to assign clusters to the sample from which they originated.
After base calling and de-multiplexing, the software generates FASTQ files that contain sequence and quality information. Due to the massively parallel nature of the SBS biochemistry, hundreds of independent sequencing reads, each with their own quality score, are generated for each amplicon in each sample. The FASTO file which is a widely accepted text based format for storing both a nucleotide sequence and its corresponding quality score. FASTQ files serve as input files for various sequence alignment and subsequent variant calling algorithms.
The MiSeqDx has a sequence alignment and variant calling program available for use.
- 3. Modes of Operation:
The MiSeqDx is a high throughput nucleic acid analyzer.
### 4. Specimen Identification:
Samples up to 96 unique specimens can be analyzed. Eight unique index primer sequences (forward), named i5 primers, and 12 unique index primer (reverse) sequences, named i7 primers, are provided. These 8 unique forward index primers and 12 unique reverse index primers, when combined in a pair wise manner, produce 96 unique index combinations allowing for up to 96 samples to be processed in parallel during the library preparation process. These are added during the library preparation process. The sample sheet, a file that the user provides the software, contains the link between each of the sample names and their associated index sequences.
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After completion of the sequence run, MiSeq Reporter software de-multiplexes the samples using the index sequences and creates FASTQ files as the data analysis output. The user can also utilize the MiSeq Reporter Software for sequence alignment and variant calling.
## 5. Specimen Sampling and Handling:
The MiSeqDx specimen is a pooled library (or libraries) derived from genomic DNA extracted from peripheral whole blood that then undergoes the following steps to create the pooled library: the genomic DNA is quantified and then used to make a library, the library sample is processed to remove remaining library preparation reagents (e.g. unused primers), normalized, and then pooled for input on the analyzer. Library normalization is used to ensure that each library is equally represented in the pooled sample.
At a minimum, eight samples must be present. If six unique samples (excluding the positive and negative controls) are not available, it is acceptable to fill the run with sample replicates or any human genomic DNA sample.
- 6. Calibration:
There is no end-user calibration of the system. During installation of the platform, a company representative (Field Applications Scientist) begins a series of tests to validate the performance of the instrument subsystems, which include optical alignment, fluidic delivery, and thermal calibration, among others. In the case of a test failure, the MiSeqDx company representative uses a set of instrument-specific tools to adjust and/or repair the instrument to meet operational specifications. Re-calibration occurs during the preventive maintenance visit.
- 7. Quality Control:
A PhiX internal control (i.e. genomic DNA from the bacteriophage ΦΧ174) is added to each pooled library prior to placement on the instrument. Successful sequencing of the PhiX genome indicates that the sequencing chemistry worked as expected. A negative control, or no template control, (not provided by the sponsor) should be included in every run in order to detect the presence of contamination in the environment or run.
- 8. Software:
FDA has reviewed applicant's Hazard Analysis and Software Development processes for this line of product types:
Yes X
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#### H. Substantial Equivalence Information:
- 1. Predicate Device Name(s) and 510(k) numbers:
Not applicable.
- 2. Comparison with Predicate Device:
Not applicable.
#### I. Special Control/Guidance Document Referenced (if applicable):
Not applicable.
#### J. Performance Characteristics:
- 1. Analytical Performance:
- a. Accuracy:
Three accuracy studies were conducted.
Study 1: This accuracy study used a representative assay designed to query a variety of genes covering 24,434 bases across 19 different chromosomes, and containing potentially clinically relevant exons. The 13 unique samples used in this study are from two parents and 11 children that have been frequently sequenced by multiple laboratories and sequencing methodologies. There are six samples from females and seven from males.
Accuracy was determined for single nucleotide variants (SNVs) by comparing the study data to well-characterized composite reference information. The reference database sequence was derived from the combination of multiple sequencing methodologies, publicly available data, and hereditary information. The following table to evaluate accuracy of the system was compiled based on data from the first run in the study. No repeat testing was done for this study.
| Amp-<br>licon | Chr. | Ana-<br>lyzed<br>frag-<br>ment<br>size1 | Amplicon<br>Genomic<br>Content | # of<br>unique<br>samples | total # of<br>samples<br>analyzed2 | #<br>calls/<br>sample<br>that<br>could<br>be<br>made3 | # of<br>no<br>calls4 | # of<br>correct<br>calls/<br>sample5 | # in-<br>correct<br>calls6 | %<br>correct<br>calls7 |
|---------------|------|-----------------------------------------|------------------------------------------------------------------------------------|---------------------------|------------------------------------|-------------------------------------------------------|----------------------|--------------------------------------|----------------------------|------------------------|
| 1 | 1 | 132 | Poly C (5),<br>63% GC | 13 | 15 | 132 | 0 | 132 | 0 | 100.00 |
| 2 | 1 | 128 | Poly T (5) | 13 | 15 | 128 | 0 | 128 | 0 | 100.00 |
| 3 | 2 | 133 | - | 13 | 15 | 133 | 0 | 133 | 0 | 100.00 |
| Amp-<br>licon | Chr. | Ana-<br>lyzed<br>frag-<br>ment<br>size¹ | Amplicon<br>Genomic<br>Content | # of<br>unique<br>samples | total # of<br>samples<br>analyzed² | #<br>calls/<br>sample<br>that<br>could<br>be<br>made³ | # of<br>no<br>calls⁴ | # of<br>correct<br>calls/<br>sample⁵ | # in-<br>correct<br>calls⁶ | %<br>correct<br>calls⁷ |
| 4 | 2 | 119 | - | 13 | 15 | 119 | 0 | 119 | 0 | 100.00 |
| 5 | 2 | 127 | Poly T (5) | 13 | 15 | 127 | 0 | 127 | 0 | 100.00 |
| 6 | 2 | 135 | Poly A (6) | 13 | 15 | 135 | 0 | 135 | 0 | 100.00 |
| 7 | 2 | 122 | Poly T (5),<br>Poly C (5) | 13 | 15 | 122 | 0 | 122 | 0 | 100.00 |
| 8 | 2 | 110 | Poly T (5) | 13 | 15 | 110 | 0 | 110 | 0 | 100.00 |
| 9⁸ | 2 | 131 | Poly A (14) | 13 | 15 | 130-<br>131 | 0 | 130-<br>131 | 9 | 99.54 |
| 10 | 2 | 117 | - | 13 | 15 | 117 | 0 | 117 | 0 | 100.00 |
| 11 | 2 | 121 | - | 13 | 15 | 121 | 0 | 121 | 0 | 100.00 |
| 12 | 2 | 114 | - | 13 | 15 | 114 | 0 | 114 | 0 | 100.00 |
| 13 | 2 | 129 | Poly A (5) | 13 | 15 | 129 | 0 | 129 | 0 | 100.00 |
| 14 | 3 | 131 | Poly A (5),<br>Poly T (5) | 13 | 15 | 131 | 0 | 131 | 0 | 100.00 |
| 15 | 3 | 130 | - | 13 | 15 | 130 | 0 | 130 | 0 | 100.00 |
| 16 | 3 | 130 | - | 13 | 15 | 130 | 0 | 130 | 0 | 100.00 |
| 17 | 3 | 117 | - | 13 | 15 | 117 | 0 | 117 | 0 | 100.00 |
| 18 | 3 | 136 | Poly T (5) | 13 | 15 | 136 | 0 | 136 | 0 | 100.00 |
| 19 | 3 | 131 | Poly T (5),<br>SNV | 13 | 15 | 131 | 0 | 131 | 0 | 100.00 |
| 20 | 3 | 123 | Poly A (5) | 13 | 15 | 123 | 0 | 123 | 0 | 100.00 |
| 21 | 3 | 117 | Poly A (6),<br>Poly T (5),<br>Homologous<br>region on a<br>different<br>chromosome | 13 | 15 | 117 | 0 | 117 | 0 | 100.00 |
| 22 | 3 | 119 | Homologous<br>region on a<br>different<br>chromosome | 13 | 15 | 119 | 0 | 119 | 0 | 100.00 |
| 23 | 3 | 120 | - | 13 | 15 | 120 | 0 | 120 | 0 | 100.00 |
| 24 | 3 | 129 | Poly T (5) | 13 | 15 | 129 | 0 | 129 | 0 | 100.00 |
| 25 | 4 | 133 | Poly C (7),<br>66% GC | 13 | 15 | 133 | 0 | 133 | 0 | 100.00 |
| 26 | 4 | 135 | Poly C (5),<br>60% GC | 13 | 15 | 135 | 0 | 135 | 0 | 100.00 |
| Amp-<br>licon | Chr. | Analyzed<br>fragment<br>size1 | Amplicon<br>Genomic<br>Content | # of<br>unique<br>samples | total # of<br>samples<br>analyzed2 | # calls/<br>sample that<br>could be<br>made3 | # of<br>no<br>calls4 | # of<br>correct<br>calls/<br>sample5 | # in-<br>correct<br>calls6 | %<br>correct<br>calls7 |
| 27 | 4 | 123 | SNV | 13 | 15 | 123 | 0 | 123 | 0 | 100.00 |
| 28 | 4 | 134 | - | 13 | 15 | 134 | 0 | 134 | 0 | 100.00 |
| 29 | 4 | 132 | - | 13 | 15 | 132 | 0 | 132 | 0 | 100.00 |
| 30 | 4 | 121 | Poly A (5),<br>SNV | 13 | 15 | 121 | 0 | 121 | 0 | 100.00 |
| 31 | 4 | 125 | - | 13 | 15 | 125 | 0 | 125 | 0 | 100.00 |
| 32 | 4 | 134 | Poly T (5) | 13 | 15 | 134 | 0 | 134 | 0 | 100.00 |
| 33 | 4 | 118 | - | 13 | 15 | 118 | 0 | 118 | 0 | 100.00 |
| 34 | 4 | 122 | Poly A (5) | 13 | 15 | 122 | 0 | 122 | 0 | 100.00 |
| 35 | 4 | 131 | - | 13 | 15 | 131 | 0 | 131 | 0 | 100.00 |
| 36 | 4 | 133 | - | 13 | 15 | 133 | 0 | 133 | 0 | 100.00 |
| 37 | 4 | 128 | Poly T (6) | 13 | 15 | 128 | 0 | 128 | 0 | 100.00 |
| 38 | 4 | 131 | - | 13 | 15 | 131 | 0 | 131 | 0 | 100.00 |
| 39 | 4 | 129 | Poly A (5),<br>Poly T (5),<br>SNV | 13 | 15 | 129 | 0 | 129 | 0 | 100.00 |
| 40 | 4 | 133 | Poly T (5),<br>SNV | 13 | 15 | 133 | 0 | 133 | 0 | 100.00 |
| 41 | 4 | 112 | SNV | 13 | 15 | 112 | 0 | 112 | 0 | 100.00 |
| 42 | 4 | 133 | - | 13 | 15 | 133 | 0 | 133 | 0 | 100.00 |
| 43 | 4 | 135 | - | 13 | 15 | 135 | 0 | 135 | 0 | 100.00 |
| 44 | 4 | 122 | - | 13 | 15 | 122 | 0 | 122 | 0 | 100.00 |
| 45 | 4 | 117 | - | 13 | 15 | 117 | 0 | 117 | 0 | 100.00 |
| 46 | 4 | 124 | - | 13 | 15 | 125 | 0 | 125 | 0 | 100.00 |
| 47 | 4 | 117 | Poly T (5) | 13 | 15 | 117 | 0 | 117 | 0 | 100.00 |
| 48 | 4 | 128 | Poly A (7) | 13 | 15 | 128 | 0 | 128 | 0 | 100.00 |
| 49 | 4 | 123 | Poly A (6) | 13 | 15 | 123 | 0 | 123 | 0 | 100.00 |
| 50 | 4 | 133 | - | 13 | 15 | 133 | 0 | 133 | 0 | 100.00 |
| 51 | 4 | 112 | - | 13 | 15 | 112 | 0 | 112 | 0 | 100.00 |
| 52 | 4 | 129 | - | 13 | 15 | 129 | 0 | 129 | 0 | 100.00 |
| 53 | 4 | 126 | - | 13 | 15 | 126 | 0 | 126 | 0 | 100.00 |
| 54 | 4 | 132 | - | 13 | 15 | 132 | 0 | 132 | 0 | 100.00 |
| 55 | 5 | 131 | - | 13 | 15 | 131 | 0 | 131 | 0 | 100.00 |
| 56 | 5 | 119 | - | 13 | 15 | 119 | 0 | 119 | 0 | 100.00 |
| 57 | 5 | 120 | Poly A (5) | 13 | 15 | 120 | 0 | 120 | 0 | 100.00 |
| 58 | 5 | 119 | - | 13 | 15 | 119 | 0 | 119 | 0 | 100.00 |
| Amp-<br>licon | Chr. | Ana-<br>lyzed<br>frag-<br>ment<br>size1 | Amplicon<br>Genomic<br>Content | # of<br>unique<br>samples | total # of<br>samples<br>analyzed2 | #<br>calls/<br>sample<br>that<br>could<br>be<br>made3 | # of<br>no<br>calls4 | # of<br>correct<br>calls/<br>sample5 | # in-<br>correct<br>calls6 | %<br>correct<br>calls7 |
| 59 | 5 | 118 | - | 13 | 15 | 118 | 0 | 118 | 0 | 100.00 |
| 60 | 5 | 112 | - | 13 | 15 | 112 | 0 | 112 | 0 | 100.00 |
| 61 | 5 | 120 | - | 13 | 15 | 120 | 0 | 120 | 0 | 100.00 |
| 62 | 5 | 120 | Poly A (5) | 13 | 15 | 120 | 0 | 120 | 0 | 100.00 |
| 63 | 5 | 115 | CT(5) | 13 | 15 | 115 | 0 | 115 | 0 | 100.00 |
| 64 | 5 | 112 | SNV | 13 | 15 | 112 | 0 | 112 | 0 | 100.00 |
| 65 | 5 | 135 | Poly T (6) | 13 | 15 | 135 | 0 | 135 | 0 | 100.00 |
| 66 | 5 | 131 | 63% GC | 13 | 15 | 131 | 0 | 131 | 0 | 100.00 |
| 67 | 5 | 121 | - | 13 | 15 | 121 | 0 | 121 | 0 | 100.00 |
| 68 | 5 | 132 | Poly A (6),<br>Poly T (8) | 13 | 15 | 132 | 0 | 132 | 0 | 100.00 |
| 69 | 7 | 133 | - | 13 | 15 | 133 | 0 | 133 | 0 | 100.00 |
| 70 | 7 | 120 | 60% GC | 13 | 15 | 120 | 0 | 120 | 0 | 100.00 |
| 71 | 7 | 135 | - | 13 | 15 | 135 | 0 | 135 | 0 | 100.00 |
| 72 | 7 | 126 | Poly A (5),<br>59% GC | 13 | 15 | 126 | 0 | 126 | 0 | 100.00 |
| 73 | 7 | 134 | - | 13 | 15 | 134 | 0 | 134 | 0 | 100.00 |
| 74 | 7 | 122 | Poly C (5),<br>63% GC | 13 | 15 | 122 | 0 | 122 | 0 | 100.00 |
| 75 | 7 | 127 | 59% GC;<br>SNV | 13 | 15 | 127 | 0 | 127 | 0 | 100.00 |
| 76 | 7 | 123 | - | 13 | 15 | 123 | 0 | 123 | 0 | 100.00 |
| 77 | 7 | 125 | - | 13 | 15 | 125 | 0 | 125 | 0 | 100.00 |
| 78 | 7 | 133 | Poly A (5),<br>Poly T (5) | 13 | 15 | 133 | 0 | 133 | 0 | 100.00 |
| 79 | 7 | 116 | - | 13 | 15 | 116 | 0 | 116 | 0 | 100.00 |
| 80 | 7 | 135 | - | 13 | 15 | 135 | 0 | 135 | 0 | 100.00 |
| 81 | 7 | 118 | - | 13 | 15 | 118 | 0 | 118 | 0 | 100.00 |
| 82 | 7 | 136 | 67% GC | 13 | 15 | 136 | 0 | 136 | 0 | 100.00 |
| 83 | 7 | 131 | 58% GC | 13 | 15 | 131 | 0 | 131 | 0 | 100.00 |
| 84 | 7 | 119 | Poly G (6),<br>61% GC | 13 | 15 | 119 | 0 | 119 | 0 | 100.00 |
| 85 | 7 | 122 | Poly T (5) | 13 | 15 | 122 | 0 | 122 | 0 | 100.00 |
| 86 | 7 | 123 | Poly A (6) | 13 | 15 | 123 | 0 | 123 | 0 | 100.00 |
| 87 | 8 | 127 | 60% GC | 13 | 15 | 127 | 0 | 127 | 0 | 100.00 |
| 88 | 8 | 129 | 57% GC | 13 | 15 | 129 | 0 | 129 | 0 | 100.00 |
| Amp-<br>licon | Chr. | Ana-<br>lyzed<br>frag-<br>ment<br>size¹ | Amplicon<br>Genomic<br>Content | # of<br>unique<br>samples | total # of<br>samples<br>analyzed² | #<br>calls/<br>sample<br>that<br>could<br>be<br>made³ | # of<br>no<br>calls4 | # of<br>correct<br>calls/<br>sample5 | # in-<br>correct<br>calls6 | %<br>correct<br>calls7 |
| 89 | 9 | 130 | Poly T (5) | 13 | 15 | 130 | 0 | 130 | 0 | 100.00 |
| 90 | 9 | 116 | - | 13 | 15 | 116 | 0 | 116 | 0 | 100.00 |
| 91 | 9 | 119 | Homologous<br>region on a<br>different<br>chromosome | 13 | 15 | 119 | 0 | 119 | 0 | 100.00 |
| 92 | 9 | 121 | - | 13 | 15 | 121 | 0 | 121 | 0 | 100.00 |
| 93 | 9 | 117 | Homologous<br>region on a<br>different<br>chromosome | 13 | 15 | 117 | 0 | 117 | 0 | 100.00 |
| 94 | 9 | 114 | - | 13 | 15 | 114 | 0 | 114 | 0 | 100.00 |
| 9510 | 9 | 129 | Poly A (14) | 13 | 15 | 130 | 0 | 129 (of<br>130) | 15 | 99.23 |
| 96 | 9 | 114 | Homologous<br>region on a<br>different<br>chromosome;<br>SNV | 13 | 15 | 114 | 0 | 114 | 0 | 100.00 |
| 97 | 9 | 122 | - | 13 | 15 | 122 | 0 | 122 | 0 | 100.00 |
| 98 | 9 | 127 | Poly A (5),<br>Poly C (5) | 13 | 15 | 127 | 0 | 127 | 0 | 100.00 |
| 99 | 9 | 133 | - | 13 | 15 | 133 | 0 | 133 | 0 | 100.00 |
| 100 | 9 | 138 | 64% GC | 13 | 15 | 138 | 0 | 138 | 0 | 100.00 |
| 101 | 9 | 139 | - | 13 | 15 | 139 | 0 | 139 | 0 | 100.00 |
| 102 | 9 | 116 | - | 13 | 15 | 116 | 0 | 116 | 0 | 100.00 |
| 103 | 9 | 133 | Poly A (5),<br>57% GC | 13 | 15 | 133 | 0 | 133 | 0 | 100.00 |
| 104 | 9 | 138 | 57% GC | 13 | 15 | 138 | 0 | 138 | 0 | 100.00 |
| 105 | 9 | 136 | Poly C (5),<br>67% GC | 13 | 15 | 136 | 0 | 136 | 0 | 100.00 |
| 106 | 9 | 118 | 70% GC | 13 | 15 | 118 | 0 | 118 | 0 | 100.00 |
| 107 | 10 | 128 | 62% GC | 13 | 15 | 128 | 0 | 128 | 0 | 100.00 |
| 108 | 10 | 120 | 60% GC | 13 | 15 | 120 | 0 | 120 | 0 | 100.00 |
| 109 | 10 | 139 | 58% GC;<br>SNV | 13 | 15 | 139 | 0 | 139 | 0 | 100.00 |
| 110 | 10 | 118 | 57% GC | 13 | 15 | 118 | 0 | 118 | 0 | 100.00 |
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| Amp-<br>licon | Chr. | Ana-<br>lyzed<br>frag-<br>ment<br>size' | Amplicon<br>Genomic<br>Content | # of<br>unique<br>samples | total # of<br>samples<br>analyzed2 | #<br>calls/<br>sample<br>that<br>could<br>be<br>made3 | # of<br>no<br>calls<br>4 | # of<br>correct<br>calls/<br>sample5 | # in-<br>correct<br>calls6 | %<br>correct<br>calls' |
|---------------|------|-----------------------------------------|--------------------------------------------------------------------------------|---------------------------|------------------------------------|-------------------------------------------------------|--------------------------|--------------------------------------|----------------------------|------------------------|
| 111 | 10 | 123 | Poly T (5) | 13 | ો ર | 123 | 0 | 123 | 0 | 100.00 |
| 112 | 10 | 121 | | 13 | ા ર | 121 | 0 | 121 | 0 | 100.00 |
| 113 | 10 | 129 | 26% GC | 13 | ો ર | 129 | 0 | 129 | 0 | 100.00 |
| 114 | 10 | 122 | | 13 | ા ર | 122 | 0 | 122 | 0 | 100.00 |
| ા । ર | 10 | 124 | Poly T (5);<br>Homologous<br>region on a<br>different<br>chromosome | 13 | ો ર | 124 | 0 | 124 | 0 | 100.00 |
| 116 | 10 | ા ૩૨ | CA(4) | 13 | ો ર | ા ૩૨ | 0 | ા રેર | 0 | 100.00 |
| 117 | 10 | ા ૩૨ | Poly A (6);<br>Homologous<br>region on a<br>different<br>chromosome | 13 | ાં ર | ા રેર | 0 | । ૩૨ | 0 | 100.00 |
| 118 | 10 | 119 | Poly C (5);<br>SNV | 13 | ા ર | 119 | 0 | 119 | 0 | 100.00 |
| 119 | 10 | ા ટેર | - | 13 | ો ર | ા ટેર | 0 | ા ટેર | 0 | 100.00 |
| 120 | 10 | 131 | - | 13 | ા ર | 131 | 0 | 131 | 0 | 100.00 |
| 121 | 10 | 117 | - | 13 | ો ર | 117 | 0 | 117 | 0 | 100.00 |
| 122 | 10 | 116 | - | 13 | ા ર | 116 | 0 | 116 | 0 | 100.00 |
| 123 | 10 | 129 | 58% GC | 13 | ો ર | 129 | 0 | 129 | 0 | 100.00 |
| 124 | 11 | 117 | Poly T (10) | 13 | ા ર | 117 | 0 | 117 | 0 | 100.00 |
| । ટેર | 11 | 117 | Poly T (5) | 13 | ો ર | 117 | 0 | 117 | 0 | 100.00 |
| 126 | 11 | 113 | Poly A (5) | 13 | ા ર | 113 | 0 | 113 | 0 | 100.00 |
| 127 | 11 | 129 | - | 13 | ા ર | 129 | 0 | 129 | 0 | 100.00 |
| 128 | 11 | 121 | Poly T (5) | 13 | ો ર | 121 | 0 | 121 | 0 | 100.00 |
| 129 | 11 | 123 | - | 13 | ો ર | 123 | 0 | 123 | 0 | 100.00 |
| 130 | 11 | 127 | Poly A (6) | 13 | ા ર | 127 | 0 | 127 | 0 | 100.00 |
| 131 | 11 | 136 | Poly T (6) | 13 | ા ર | 136 | 0 | 136 | 0 | 100.00 |
| 132 | 11 | 132 | Poly T (5) | 13 | ો ર | 132 | 0 | 132 | 0 | 100.00 |
| 133 | 11 | ા । ર | | 13 | ો ર | ા । ર | 0 | ા । ર | 0 | 100.00 |
| 134 | 11 | 117 | Poly T (8);<br>19% GC | 13 | ા ર | 117 | 0 | 117 | 0 | 100.00 |
| ા રેર | 11 | 134 | Poly A (5);<br>Poly T (5) | 13 | ા ર | 134 | 0 | 134 | 0 | 100.00 |
| 136 | 11 | 131 | Poly A (5) | । ਤੇ | ા ર | 131 | 0 | 131 | 0 | 100.00 |
| Amp-<br>licon | Chr. | Ana-<br>lyzed<br>frag-<br>ment<br>size¹ | Amplicon<br>Genomic<br>Content | # of<br>unique<br>samples | total # of<br>samples<br>analyzed² | #<br>calls/<br>sample<br>that<br>could<br>be<br>made³ | # of<br>no<br>calls<br>4 | # of<br>correct<br>calls/<br>sample5 | # in-<br>correct<br>calls6 | %<br>correct<br>calls7 |
| 137 | 11 | 133 | 26% GC;<br>SNV | 13 | 15 | 133 | 0 | 133 | 0 | 100.00 |
| 138 | 11 | 137 | Poly T (8);<br>SNV | 13 | 15 | 137 | 0 | 137 | 0 | 100.00 |
| 139 | 11 | 131 | Poly A (5) | 13 | 15 | 131 | 0 | 131 | 0 | 100.00 |
| 140 | 12 | 131 | - | 13 | 15 | 131 | 0 | 131 | 0 | 100.00 |
| 141 | 12 | 128 | - | 13 | 15 | 128 | 0 | 128 | 0 | 100.00 |
| 142 | 12 | 133 | Poly A (5) | 13 | 15 | 133 | 0 | 133 | 0 | 100.00 |
| 143 | 12 | 136 | - | 13 | 15 | 136 | 0 | 136 | 0 | 100.00 |
| 144 | 12 | 124 | - | 13 | 15 | 124 | 0 | 124 | 0 | 100.00 |
| 145 | 12 | 122 | 59% GC | 13 | 15 | 122 | 0 | 122 | 0 | 100.00 |
| 146 | 13 | 122 | - | 13 | 15 | 122 | 0 | 122 | 0 | 100.00 |
| 147 | 13 | 116 | Poly C (5) | 13 | 15 | 116 | 0 | 116 | 0 | 100.00 |
| 148 | 13 | 133 | - | 13 | 15 | 133 | 0 | 133 | 0 | 100.00 |
| 149 | 13 | 117 | SNV | 13 | 15 | 117 | 0 | 117 | 0 | 100.00 |
| 150 | 13 | 124 | Poly T (6) | 13 | 15 | 124 | 0 | 124 | 0 | 100.00 |
| 151 | 13 | 123 | Poly T (5);<br>26% GC | 13 | 15 | 123 | 0 | 123 | 0 | 100.00 |
| 152 | 13 | 115 | Poly A (5) | 13 | 15 | 115 | 0 | 115 | 0 | 100.00 |
| 153 | 13 | 125 | - | 13 | 15 | 125 | 0 | 125 | 0 | 100.00 |
| 154 | 13 | 121 | - | 13 | 15 | 121 | 0 | 121 | 0 | 100.00 |
| 155 | 13 | 123 | - | 13 | 15 | 123 | 0 | 123 | 0 | 100.00 |
| 156 | 13 | 114 | - | 13 | 15 | 114 | 0 | 114 | 0 | 100.00 |
| 157 | 13 | 119 | - | 13 | 15 | 119 | 0 | 119 | 0 | 100.00 |
| 158 | 14 | 122 | 58% GC | 13 | 15 | 122 | 0 | 122 | 0 | 100.00 |
| 159 | 16 | 122 | - | 13 | 15 | 122 | 0 | 122 | 0 | 100.00 |
| 160 | 16 | 121 | - | 13 | 15 | 121 | 0 | 121 | 0 | 100.00 |
| 161 | 16 | 123 | Poly C (5) | 13 | 15 | 123 | 0 | 123 | 0 | 100.00 |
| 162 | 17 | 119 | - | 13 | 15 | 119 | 0 | 119 | 0 | 100.00 |
| 163 | 17 | 119 | 61% GC | 13 | 15 | 119 | 0 | 119 | 0 | 100.00 |
| 164 | 17 | 135 | - | 13 | 15 | 135 | 0 | 135 | 0 | 100.00 |
| 165 | 17 | 116 | Poly C (6);<br>60% GC;<br>SNV | 13 | 15 | 116 | 0 | 116 | 0 | 100.00 |
| 166 | 17 | 123 | - | 13 | 15 | 123 | 0 | 123 | 0 | 100.00 |
| 167 | 17 | 116 | 62% GC | 13 | 15 | 116 | 0 | 116 | 0 | 100.00 |
| Amp-<br>licon | Chr. | Ana-<br>lyzed<br>frag-<br>ment<br>size¹ | Amplicon<br>Genomic<br>Content | # of<br>unique<br>samples | total # of<br>samples<br>analyzed² | #<br>calls/<br>sample<br>that<br>could<br>be<br>made³ | # of<br>no<br>calls<br>4 | # of<br>correct<br>calls/<br>sample5 | # in-<br>correct<br>calls6 | %<br>correct<br>calls7 |
| 168 | 17 | 118 | Poly C (5);<br>65% GC | 13 | 15 | 118 | 0 | 118…
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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.