K242403 · Canon Medical Systems Corporation · JAK · Dec 23, 2024 · Radiology
Device Facts
Record ID
K242403
Device Name
Aquilion ONE (TSX-308A/3) V1.5
Applicant
Canon Medical Systems Corporation
Product Code
JAK · Radiology
Decision Date
Dec 23, 2024
Decision
SESE
Submission Type
Traditional
Regulation
21 CFR 892.1750
Device Class
Class 2
Attributes
AI/ML
AI Performance
Output
Algorithm
Acceptance
Observed
Dev DS
Dev Readers
Test DS
Test Readers
Lung image quality
Deep Convolutional Network
—
Equivalent or improved performance compared to predicate device.
—
—
Bench testing using phantoms and clinical images.
—
Body image quality
Deep Convolutional Network
—
Equivalent or improved performance compared to predicate device.
—
—
Bench testing using phantoms and clinical images.
—
Cardiac image quality
Deep Convolutional Network
—
Equivalent or improved performance compared to predicate device.
—
—
Bench testing using phantoms and clinical images.
—
Motion artifact reduction
Deep Convolutional Network
—
Motion artifacts significantly reduced and CT Numbers maintained.
—
—
Bench testing using phantoms and clinical images.
—
Indications for Use
This device is indicated to acquire and display cross sectional volumes of the whole body, to include the head, with the capability to image whole organs in a single rotation. Whole organs include but are not limited to brain, heart, pancreas, etc. The Aquilion ONE has the capability to provide volume sets of the entire organ. These volume sets can be used to perform specialized studies, using indicated software/hardware, of the whole organ by a trained and qualified physician. FIRST is an iterative reconstruction algorithm intended to reduce exposure dose and improve high contrast spatial resolution for abdomen, pelvis, chest, cardiac, extremities and head applications. AiCE is a noise reduction algorithm that improves image quality and reduces image noise by employing Deep Convolutional Network methods for abdomen, pelvis, lung, cardiac, extremities, head, and inner ear applications. The spectral imaging system allows the system to acquire two nearly simultaneous CT images of an anatomical location using distinct tube voltages and/or tube currents by rapid KV switching. The Xray dose will be the sum of the dose at each respective tube voltage and current in a rotation. Information regarding the material composition of various organs, tissues, and contrast materials may be gained from the differences in X-ray attenuation between these distinct energies. When used by a qualified physician, a potential application is to determine the course of treatment. PIQE* is a Deep Learning Reconstruction method designed to enhance spatial resolution. By incorporating noise reduction into the Deep Convolutional Network (DCNN), it is possible to achieve both spatial resolution improvement and noise reduction for cardiac, abdomen and pelvis, and lung applications, in comparison to FBP and hybrid iterative reconstruction. CLEAR Motion is a Deep Learning Reconstruction (DLR) method designed to reduce motion artifacts. A Deep Convolutional Network (DCNN) is used to estimate the patient's motion. This information is used in the reconstruction process to obtain lung images with less motion artifacts.
Device Story
Aquilion ONE (TSX-308A/3) V1.5 is a whole-body multi-slice helical CT scanner comprising gantry, couch, and console. It acquires cross-sectional volume data via X-ray; transforms raw data into images using iterative reconstruction (FIRST), noise reduction (AiCE), and deep learning reconstruction (PIQE, CLEAR Motion). Used in clinical settings by physicians/technicians. CLEAR Motion uses DCNN to estimate patient motion, reducing artifacts in lung images. Spectral imaging uses rapid KV switching for material composition analysis. Output is diagnostic-quality cross-sectional images; assists physicians in clinical decision-making and treatment planning. Benefits include reduced exposure dose, improved spatial resolution, and reduced motion artifacts.
Clinical Evidence
Bench testing only. Evaluated image quality using phantoms (Contrast-to-Noise Ratios, CT Number Accuracy, Uniformity, Slice Sensitivity Profile, MTF, Noise Power Spectra). CLEAR Motion evaluated using thoracic dynamic phantoms (12 BPM) and clinical images, demonstrating significant motion artifact reduction. Representative diagnostic images (body, cardiac, chest, head, extremity) reviewed by American Board-Certified Radiologists confirmed diagnostic quality.
Technological Characteristics
Multi-slice helical CT scanner. Materials conform to ISO 13485. Standards: IEC 60601-1, 60601-1-3, 60601-2-44, NEMA XR-25/26/29. Features: FIRST (iterative reconstruction), AiCE (DCNN noise reduction), PIQE (DCNN spatial resolution/noise reduction), CLEAR Motion (DCNN motion correction), and spectral imaging (rapid KV switching). Connectivity: Networked. Software: DCNN-based reconstruction algorithms.
Indications for Use
Indicated for whole-body cross-sectional imaging, including head and whole organs (e.g., brain, heart, pancreas), in patients requiring diagnostic CT scans. Used by trained physicians for specialized studies and treatment planning.
Regulatory Classification
Identification
A computed tomography x-ray system is a diagnostic x-ray system intended to produce cross-sectional images of the body by computer reconstruction of x-ray transmission data from the same axial plane taken at different angles. This generic type of device may include signal analysis and display equipment, patient and equipment supports, component parts, and accessories.
Predicate Devices
Aquilion ONE (TSX-308A/3) V1.4 with PIQE Reconstruction System (K232835)
Reference Devices
Aquilion ONE (TSX-306A/3) V10.12 with Spectral Imaging System (K213504)
Submission Summary (Full Text)
{0}------------------------------------------------
Image /page/0/Picture/0 description: The image shows the logo of the U.S. Food and Drug Administration (FDA). The logo consists of two parts: the Department of Health & Human Services logo on the left and the FDA logo on the right. The FDA logo is a blue square with the letters "FDA" in white, followed by the words "U.S. FOOD & DRUG ADMINISTRATION" in blue.
December 23, 2024
Canon Medical Systems Corporation % Orlando Tadeo Jr Sr. Manager, Regulatory Affairs Canon Medical Systems USA, Inc. 2441 Michelle Drive TUSTIN, CA 92780
Re: K242403
Trade/Device Name: Aquilion ONE (TSX-308A/3) V1.5 Regulation Number: 21 CFR 892.1750 Regulation Name: Computed tomography x-ray system Regulatory Class: Class II Product Code: JAK Dated: November 21, 2024 Received: November 22, 2024
Dear Orlando Tadeo Jr:
We have reviewed your section 510(k) premarket notification of intent to market the device referenced above and have determined the device is substantially equivalent (for the indications for use stated in the enclosure) to legally marketed predicate devices marketed in interstate commerce prior to May 28, 1976, the enactment date of the Medical Device Amendments, or to devices that have been reclassified in accordance with the provisions of the Federal Food. Drug. and Cosmetic Act (that do not require approval of a premarket approval application (PMA). You may, therefore, market the device, subject to the general controls provisions of the Act. Although this letter refers to your product as a device, please be aware that some cleared products may instead be combination products. The 510(k) Premarket Notification Database available at https://www.accessdata.fda.gov/scripts/cdrb/cfdocs/cfpmn/pmn.cfm identifies combination product submissions. The general controls provisions of the Act include requirements for annual registration, listing of devices, good manufacturing practice, labeling, and prohibitions against misbranding and adulteration. Please note: CDRH does not evaluate information related to contract liability warranties. We remind you, however, that device labeling must be truthful and not misleading.
If your device is classified (see above) into either class II (Special Controls) or class III (PMA), it may be subject to additional controls. Existing major regulations affecting your device can be found in the Code of Federal Regulations, Title 21, Parts 800 to 898. In addition, FDA may publish further announcements concerning your device in the Federal Register.
{1}------------------------------------------------
Additional information about changes that may require a new premarket notification are provided in the FDA guidance documents entitled "Deciding When to Submit a 510(k) for a Change to an Existing Device" (https://www.fda.gov/media/99812/download) and "Deciding When to Submit a 510(k) for a Software Change to an Existing Device" (https://www.fda.gov/media/99785/download).
Your device is also subject to, among other requirements, the Quality System (QS) regulation (21 CFR Part 820), which includes, but is not limited to, 21 CFR 820.30. Design controls; 21 CFR 820.90. Nonconforming product; and 21 CFR 820.100, Corrective and preventive action. Please note that regardless of whether a change requires premarket review, the QS regulation requires device manufacturers to review and approve changes to device design and production (21 CFR 820.30 and 21 CFR 820.70) and document changes and approvals in the device master record (21 CFR 820.181).
Please be advised that FDA's issuance of a substantial equivalence determination does not mean that FDA has made a determination that your device complies with other requirements of the Act or any Federal statutes and regulations administered by other Federal agencies. You must comply with all the Act's requirements, including, but not limited to: registration and listing (21 CFR Part 807); labeling (21 CFR Part 801); medical device reporting of medical device-related adverse events) (21 CFR Part 803) for devices or postmarketing safety reporting (21 CFR Part 4, Subpart B) for combination products (see https://www.fda.gov/combination-products/guidance-regulatory-information/postmarketing-safety-reportingcombination-products); good manufacturing practice requirements as set forth in the quality systems (QS) regulation (21 CFR Part 820) for devices or current good manufacturing practices (21 CFR Part 4, Subpart A) for combination products; and, if applicable, the electronic product radiation control provisions (Sections 531-542 of the Act); 21 CFR Parts 1000-1050.
All medical devices, including Class I and unclassified devices and combination product device constituent parts are required to be in compliance with the final Unique Device Identification System rule ("UDI Rule"). The UDI Rule requires, among other things, that a device bear a unique device identifier (UDI) on its label and package (21 CFR 801.20(a)) unless an exception or alternative applies (21 CFR 801.20(b)) and that the dates on the device label be formatted in accordance with 21 CFR 801.18. The UDI Rule (21 CFR 830.300(a) and 830.320(b)) also requires that certain information be submitted to the Global Unique Device Identification Database (GUDID) (21 CFR Part 830 Subpart E). For additional information on these requirements, please see the UDI System webpage at https://www.fda.gov/medical-device-advicecomprehensive-regulatory-assistance/unique-device-identification-system-udi-system.
Also, please note the regulation entitled, "Misbranding by reference to premarket notification" (21 CFR 807.97). For questions regarding the reporting of adverse events under the MDR regulation (21 CFR Part 803), please go to https://www.fda.gov/medical-device-safety/medical-device-reportingmdr-how-report-medical-device-problems.
For comprehensive regulatory information about medical devices and radiation-emitting products, including information about labeling regulations, please see Device Advice (https://www.fda.gov/medicaldevices/device-advice-comprehensive-regulatory-assistance) and CDRH Learn (https://www.fda.gov/training-and-continuing-education/cdrh-learn). Additionally, you may contact the Division of Industry and Consumer Education (DICE) to ask a question about a specific regulatory topic. See the DICE website (https://www.fda.gov/medical-device-advice-comprehensive-regulatory
{2}------------------------------------------------
assistance/contact-us-division-industry-and-consumer-education-dice) for more information or contact DICE by email (DICE@fda.hhs.gov) or phone (1-800-638-2041 or 301-796-7100).
Sincerely,
Gabriela M. Digitally signed by Rodal -S Gabriela M. Rodal -S for
Lu Jiang, Ph.D. Assistant Director Diagnostic X-ray Systems Team DHT8B: Division of Radiologic Imaging Devices and Electronic Products OHT8: Office of Radiological Health Office of Product Evaluation and Quality Center for Devices and Radiological Health
Enclosure
{3}------------------------------------------------
# Indications for Use
Submission Number (if known)
K242403
Device Name
Aquilion ONE (TSX-308A/3) V1.5
## Indications for Use (Describe)
This device is indicated to acquire and display cross sectional volumes of the whole body, to include the head, with the capability to image whole organs in a single rotation. Whole organs include but are not limited to brain, heart, pancreas, etc. The Aquilion ONE has the capability to provide volume sets of the entire organ. These volume sets can be used to perform specialized studies, using indicated software/hardware, of the whole organ by a trained and qualified physician.
FIRST is an iterative reconstruction algorithm intended to reduce exposure dose and improve high contrast spatial resolution for abdomen, pelvis, chest, cardiac, extremities and head applications.
AiCE is a noise reduction algorithm that improves image quality and reduces image noise by employing Deep Convolutional Network methods for abdomen, pelvis, lung, cardiac, extremities, head, and inner ear applications.
The spectral imaging system allows the system to acquire two nearly simultaneous CT images of an anatomical location using distinct tube voltages and/or tube currents by rapid KV switching. The Xray dose will be the sum of the dose at each respective tube voltage and current in a rotation. Information regarding the material composition of various organs, tissues, and contrast materials may be gained from the differences in X-ray attenuation between these distinct energies. When used by a qualified physician, a potential application is to determine the course of treatment.
PIQE* is a Deep Learning Reconstruction method designed to enhance spatial resolution. By incorporating noise reduction into the Deep Convolutional Network (DCNN), it is possible to achieve both spatial resolution improvement and noise reduction for cardiac, abdomen and pelvis, and lung applications, in comparison to FBP and hybrid iterative reconstruction.
CLEAR Motion is a Deep Learning Reconstruction (DLR) method designed to reduce motion artifacts. A Deep Convolutional Network (DCNN) is used to estimate the patient's motion. This information is used in the reconstruction process to obtain lung images with less motion artifacts.
Type of Use (Select one or both, as applicable)
Prescription Use (Part 21 CFR 801 Subpart D)
Over-The-Counter Use (21 CFR 801 Subpart C)
## CONTINUE ON A SEPARATE PAGE IF NEEDED.
{4}------------------------------------------------
This section applies only to requirements of the Paperwork Reduction Act of 1995.
## *DO NOT SEND YOUR COMPLETED FORM TO THE PRA STAFF EMAIL ADDRESS BELOW.*
The burden time for this collection of information is estimated to average 79 hours per response, including the time to review instructions, search existing data sources, gather and maintain the data needed and complete and review the collection of information. Send comments regarding this burden estimate or any other aspect of this information collection, including suggestions for reducing this burden, to:
> Department of Health and Human Services Food and Drug Administration Office of Chief Information Officer Paperwork Reduction Act (PRA) Staff PRAStaff(@fda.hhs.gov
"An agency may not conduct or sponsor, and a person is not required to respond to, a collection of information unless it displays a currently valid OMB number."
{5}------------------------------------------------
## 510(k) SUMMARY
1. SUBMITTER'S NAME: Fumiaki Teshima Senior Manager, Quality Assurance Department Canon Medical Systems Corporation 1385 Shimoishigami Otawara-Shi, Tochigi-ken, Japan 324-8550
- 2. ESTABLISHMENT REGISTRATION: 9614698
#### 3. OFFICIAL CORRESPONDENT/CONTACT PERSON:
Orlando Tadeo, Jr. Sr. Manager, Regulatory Affairs Canon Medical Systems USA, Inc 2441 Michelle Drive Tustin, CA 92780 (714) 669-7459
4. DATE PREPARED: August 12, 2024
- 5. TRADE NAME(S): Aquilion ONE (TSX-308A/3) V1.5
- COMMON NAME: 6. Computed Tomography X-ray System
#### 7. DEVICE CLASSIFICATION:
a) Classification Name: Computed Tomography X-ray system b) Regulation Number: 21 CFR §892.1750 c) Regulatory Class: Class II
#### 8. PRODUCT CODE:
JAK
#### 9. PERFORMANCE STANDARD:
This device conforms to applicable Performance Standards for Ionizing Radiation Emitting Products [21 CFR, Subchapter J, Part 1020]
PHONE: 800-421-1968 2441 Michelle Drive, Tustin, CA 92780
{6}------------------------------------------------
#### 10. PREDICATE DEVICE:
| Product | Marketed by | Regulation<br>Number | Regulation<br>Name | Product Code | 510(k)<br>Number | Clearance<br>Date |
|--------------------------------------------------------------------------|----------------------------|----------------------|----------------------------------------|---------------------------------------------------|------------------|-------------------|
| Primary<br>Predicate Device | | | | | | |
| Aquilion ONE (TSX-<br>308A/3) V1.4 with<br>PIQE Reconstruction<br>System | Canon Medical Systems, USA | 21 CFR<br>§892.1750 | Computed<br>Tomography<br>X-ray System | JAK:<br>System, X-ray,<br>Tomography,<br>Computed | K232835 | April 2, 2024 |
| Reference<br>Predicate Device | | | | | | |
| Aquilion ONE (TSX-<br>306A/3) V10.12 with<br>Spectral Imaging<br>System | Canon Medical Systems, USA | 21 CFR<br>§892.1750 | Computed<br>Tomography<br>X-ray System | JAK:<br>System, X-ray,<br>Tomography,<br>Computed | K213504 | June 16, 2022 |
#### 11. REASON FOR SUBMISSION:
Modification of existing medical device
#### DEVICE DESCRIPTION: 12.
Aquilion ONE (TSX-308A/3) V1.5 is a whole body multi-slice helical CT scanner, consisting of a gantry, couch and a console used for data processing and display. This device captures cross sectional volume data sets used to perform specialized studies, using indicated software/hardware, by a trained and qualified physician. This system is based upon the technology and materials of previously marketed Canon CT systems.
#### 13. INDICATIONS FOR USE:
This device is indicated to acquire and display cross sectional volumes of the whole body, to include the head, with the capability to image whole organs in a single rotation. Whole organs include but are not limited to brain, heart, pancreas, etc. The Aquilion ONE has the capability to provide volume sets of the entire organ. These volume sets can be used to perform specialized studies, using indicated software/hardware, of the whole organ by a trained and qualified physician.
FIRST is an iterative reconstruction algorithm intended to reduce exposure dose and improve high contrast spatial resolution for abdomen, pelvis, chest, cardiac, extremities and head applications.
AiCE is a noise reduction algorithm that improves image quality and reduces image noise by employing Deep Convolutional Network methods for abdomen, pelvis, lung, cardiac, extremities, head, and inner ear applications.
The spectral imaging system allows the system to acquire two nearly simultaneous CT images of an anatomical location using distinct tube voltages and/or tube currents by rapid KV switching. The X-ray dose will be the sum of the dose at each respective tube voltage and current in a rotation. Information regarding the material composition of various organs, tissues, and contrast materials may be gained from the differences in X-ray attenuation between these distinct energies. When used by a qualified physician, a potential application is to determine the course of treatment.
{7}------------------------------------------------
PIQE is a Deep Learning Reconstruction method designed to enhance spatial resolution. By incorporating noise reduction into the Deep Convolutional Network (DCNN), it is possible to achieve both spatial resolution improvement and noise reduction for cardiac, abdomen and pelvis, and lung applications, in comparison to FBP and hybrid iterative reconstruction.
CLEAR Motion is a Deep Learning Reconstruction (DLR) method designed to reduce motion artifacts. A Deep Convolutional Network (DCNN) is used to estimate the patient's motion. This information is used in the reconstruction process to obtain lung images with less motion artifacts.
#### 14. SUBSTANTIAL EQUIVALENCE:
The Aquilion ONE (TSX-308A/3) V1.5 is substantially equivalent to Aquilion ONE (TSX-308A/3) V1.4 with PIQE Reconstruction System, which received premarket clearance under K232835, and is marketed by Canon Medical Systems USA. The intended use of the Aquilion ONE is the same as that of the predicate device. A comparison of the technological characteristics between the subject and the predicate device is included below.
| | Subject Device | Predicate Device |
|----------------------------------------------------------------|---------------------------------------------------------------------------------------|----------------------------------------------------------------|
| Device Name, Model Number | Aquilion ONE (TSX-308A/3) V1.5 | Aquilion ONE (TSX-308A/3) V1.4 with PIQE Reconstruction System |
| 510(k) Number | This submission | K232835 |
| PIQE Reconstruction System (CRRS-001A) | Available | Available |
| ■ Scan Regions | ■ Cardiac, Lung, Abdomen and pelvis (Body) | ■ Cardiac, Abdomen and pelvis (Body) |
| ■ Scan Type | ■ Volume scan, Dynamic volume scan, Helical scan | ■ Volume scan, Dynamic volume scan, Helical scan |
| 3D Landmark Scan | Available X-ray tube voltage: 120kV | Available X-ray tube voltage: 120/135kV |
| Spectral Imaging System (CSDE-004A) | Option:<br>BODY, LUNG, BONE and CARDIAC<br>(Cardiac previously cleared under K213504) | Option:<br>BODY, LUNG and BONE |
| Motion Correction<br>Advanced Patient Motion Correction (APMC) | Available | Available |
| CLEAR Motion (CSCM-001A) | Available | Not Available |
| Connect Assistance (COCA-001A) | Available<br>(Previously cleared under K213504) | Not Available |
| Area Finder (CGAP-003A) | Available<br>(Previously cleared under K213504) | Not Available |
#### 15. SAFETY:
The device is designed and manufactured under the Quality System Regulations as outlined in 21 CFR § 820 and ISO 13485 Standards. This device is in conformance with the applicable parts of the following standards IEC60601-1, IEC60601-1-3, IEC60601-1-6, IEC60601-1-6, IEC60601-1-9, IEC60601-2-28, IEC60601-2-44, IEC60825-1, IEC62304, IEC81001-5-1, IEC62366-1, NEMA XR-25, NEMA XR-26 and NEMA XR-29. Additionally, this device complies with all applicable requirements of the radiation safety performance standards, as outlined in 21 CFR §1010 and §1020.
{8}------------------------------------------------
#### 16. TESTING
Risk analysis and verification/validation activities conducted through bench testing demonstrate that the established specifications for the device have been met.
## Performance Testing - Bench
### Image Quality Evaluations
CT image quality assessments were performed, utilizing phantoms, to evaluate PIQE Lung Image Quality, PIQE Body Image Quality and Spectral Cardiac Image Quality of the TSX-308A (Aquilion ONE) system relative to the predicate device, TSX-306A (Aquilion Prism) with regard to Contrastto-Noise Ratios, CT Number Accuracy, Uniformity, Slice Sensitivity Profile, Modulation Transfer Function, Standard Deviation of Noise Power Spectra, and Low Contrast Detectability. It was concluded that the subject device demonstrated equivalent or improved performance, compared to the predicate device, as demonstrated by the results of the above testing.
### CLEAR Motion Evaluations
Performance tests were conducted to evaluate the performance CLEAR Motion, utilizing phantoms and clinical images. These studies included comparing clinical lung images, a water phantom and a thoracic dynamic phantom (evaluated at 12 BPM), reconstructed with AIDR3D, AiCE and/or FBP with and without CLEAR Motion applied. Conclusions from these studies demonstrated that CLEAR Motion performed as intended, in that motion artifacts were significantly reduced and CT Numbers were maintained, compared to standard reconstructed images in which CLEAR Motion was not applied.
## Performance Testing - Clinical Images
Representative body, cardiac, chest, head, and extremity diagnostic images, reviewed by American Board-Certified Radiologists, were obtained using the subject device and it was confirmed that the reconstructed images using the subject device were of diagnostic quality.
A summary of the risk analysis and verification/validation testing conducted through bench testing is included in this submission which demonstrates that the requirements for the system have been met.
Software Documentation for a Basic Documentation Level, per the FDA guidance document, "Content of Premarket Submissions for Device Software Functions" issued on June 14, 2023, is included in this submission. This documentation includes justification for the Basic Documentation Level determination as well as testing which demonstrates that the verification and validation requirements have been met.
Cybersecurity documentation, per the FDA guidance document "Cybersecurity in Medical Devices: Quality System Considerations and Content of Premarket Submissions", issued on September 27, 2023, was included in this submission.
#### 17. CONCLUSION
The Aquilion ONE (TSX-308A/3) V1.5 performs in a manner similar to and is intended for the same use as the predicate device, as indicated in product labeling. Based upon this information, conformance to standards, successful completion of software validation, application of risk management and design controls and the performance data presented in this submission it is concluded that the subject device has demonstrated substantial equivalence to the predicate device and is as safe and effective for its intended use.
Predicate graph will load when search results are available.
Embedding visualization will load when search results are available.
PDF viewer will load when search results are available.
Loading panels...
Select an item from Submissions
Click any panel, subpart, regulation, product code, or device to see details here.
Section Matches
Results will appear here.
Product Code Matches
Results will appear here.
Special Control Matches
Results will appear here.
Loading collections...
Loading
My Alerts
You will receive email notifications based on the filters and frequency you set for each alert.
Sort by:
Create Alert
Search Filters
Agent Token
Create a read-only bearer token for Claude, ChatGPT, or other agents that can call HTTP APIs.
Copy this now. It will not be shown again.
Connected apps
Apps you authorized through browser sign-in. Disconnecting revokes their access immediately.
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.