The Neuro.AI Algorithm is an algorithm for use by trained professionals, including but not limited to physicians, surgeons and medical clinicians. The Neuro.Al Algorithm is a standalone image processing software device that can be deployed as a Microsoft Windows® executable on off-the-shelf hardware or as a containerized application (e.g., a Docker container) that runs on off-the-shelf hardware or on a cloud platform. Data and images are acquired via DICOM compliant imaging devices. DICOM results may be exported, combined with or utilized by other DICOM-compliant systems and results. The Neuro.AI algorithm provides analysis capabilities for static, functional, dynamic and derived imaging datasets acquired with CT or MRI. It can be used for the analysis of dynamic brain image data, showing properties of changes in contrast over time. This functionality includes calculation of parameters related to brain tissue perfusion, vasular assessment and tissue blood volume and other parametric maps with or without the ventricles included in the calculation. The algorithm also includes volume reformat in various orientions, rotational MIP 3D batch while removing the skull. This "tumble view" allows qualitative review of vascular structure in direct correlation to the perfusion maps for comprehensive review. The results of the Neuro.AI Algorithm can be delivered to the end-user through image viewers such as TeraRecon's Aquarius iNtuition system, TeraRecon's Northstar AI Results Explorer, or other image viewing systems like PACS that can support DICOM results generated by Neuro.AI. The Neuro.AI Algorithm results are designed for use by trained healthcare professionals and are intended to assist the physician in diagnosis, who is responsible for making all final patient management decisions.
Device Story
Standalone image processing software; processes static, functional, dynamic, and derived CT/MRI brain datasets. Inputs: DICOM-compliant imaging data. Operation: Performs motion correction, calculates perfusion parameters (TTP, TOT, RT, MTT, BV/CBV, BF/CBF), generates parametric maps, and creates rotational MIP 3D 'tumble views' with skull removal. Output: DICOM-formatted results (text, parametric maps, reformatted images, 3D visualizations). Deployment: Windows executable or Docker container on off-the-shelf hardware or cloud. Usage: Used by trained healthcare professionals (physicians/clinicians) in clinical settings. Integration: Results viewed via TeraRecon Aquarius iNtuition, Northstar AI Results Explorer, or third-party PACS. Clinical impact: Assists physicians in diagnosis and decision-making; provides supporting analytical tools for vascular structure and tissue perfusion review. Benefit: Speeds decision-making and improves communication; physician judgment remains paramount.
Clinical Evidence
Bench testing only. Software verification and validation performed per IEC 62304 and ISO 14971. All predefined acceptance criteria met; no clinical data provided.
Technological Characteristics
Standalone software; Windows executable or Docker container. Supports DICOM 3.x. Mathematical modeling via SVD. Features: motion correction, perfusion parameter calculation, 2D/3D/4D visualization, rotational MIP, ventricle segmentation. Interoperability: CT/MRI scanners, PACS, EMR, TeraRecon EnvoyAI. Software lifecycle per IEC 62304.
Indications for Use
Indicated for trained professionals, including physicians, surgeons, and clinicians, to assist in the diagnosis of brain perfusion and vascular assessment using CT or MRI datasets. No specific age or gender contraindications stated.
Regulatory Classification
Identification
A medical image management and processing system is a device that provides one or more capabilities relating to the review and digital processing of medical images for the purposes of interpretation by a trained practitioner of disease detection, diagnosis, or patient management. The software components may provide advanced or complex image processing functions for image manipulation, enhancement, or quantification that are intended for use in the interpretation and analysis of medical images. Advanced image manipulation functions may include image segmentation, multimodality image registration, or 3D visualization. Complex quantitative functions may include semi-automated measurements or time-series measurements.
Special Controls
*Classification.* Class II (special controls; voluntary standards—Digital Imaging and Communications in Medicine (DICOM) Std., Joint Photographic Experts Group (JPEG) Std., Society of Motion Picture and Television Engineers (SMPTE) Test Pattern).
Predicate Devices
iNtuition-TDA, TVA and Parametric Mapping (K131447)
{0}------------------------------------------------
November 6, 2020
Image /page/0/Picture/1 description: The image contains 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 features the letters "FDA" in a blue square, followed by the words "U.S. FOOD & DRUG ADMINISTRATION" in blue text.
TeraRecon, Inc. % Mr. Patrick Willhite Director, Quality Assurance and Regulatory Affairs 4309 Emperor Blvd., Suite 310 DURHAM NC 27703
Re: K200750
Trade/Device Name: Neuro.AI Algorithm Regulation Number: 21 CFR 892.2050 Regulation Name: Picture archiving and communications system Regulatory Class: Class II Product Code: LLZ Dated: October 26, 2020 Received: October 28, 2020
Dear Mr. Willhite:
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 (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 located at https://www.accessdata.fda.gov/scripts/cdrh/cfdocs/cfpmp/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.
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 803) for devices or postmarketing safety reporting (21 CFR 4, Subpart B) for combination products (see
{1}------------------------------------------------
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 4, Subpart A) for combination products; and, if applicable, the electronic product radiation control provisions (Sections 531-542 of the Act); 21 CFR 1000-1050.
Also, please note the regulation entitled, "Misbranding by reference to premarket notification" (21 CFR Part 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-regulatoryassistance/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,
For
Thalia T. Mills, Ph.D. Diretor Division of Radiological Health OHT7: Office of In Vitro Diagnostics and Radiological Health Office of Product Evaluation and Quality Center for Devices and Radiological Health
Enclosure
{2}------------------------------------------------
# Indications for Use
510(k) Number (if known) K200750
Device Name Neuro.AI Algorithm
#### Indications for Use (Describe)
The Neuro.AI Algorithm is an algorithm for use by trained professionals, including but not limited to physicians, surgeons and medical clinicians.
The Neuro.Al Algorithm is a standalone image processing software device that can be deployed as a Microsoft Windows® executable on off-the-shelf hardware or as a containerized application (e.g., a Docker container) that runs on off-the-shelf hardware or on a cloud platform. Data and images are acquired via DICOM compliant imaging devices. DICOM results may be exported, combined with or utilized by other DICOM-compliant systems and results.
The Neuro.AI algorithm provides analysis capabilities for static, functional, dynamic and derived imaging datasets acquired with CT or MRI. It can be used for the analysis of dynamic brain image data, showing properties of changes in contrast over time. This functionality includes calculation of parameters related to brain tissue perfusion, vasular assessment and tissue blood volume and other parametric maps with or without the ventricles included in the calculation. The algorithm also includes volume reformat in various orientions, rotational MIP 3D batch while removing the skull. This "tumble view" allows qualitative review of vascular structure in direct correlation to the perfusion maps for comprehensive review.
The results of the Neuro.AI Algorithm can be delivered to the end-user through image viewers such as TeraRecon's Aquarius iNtuition system, TeraRecon's Northstar AI Results Explorer, or other image viewing systems like PACS that can support DICOM results generated by Neuro.AI.
The Neuro.AI Algorithm results are designed for use by trained healthcare professionals and are intended to assist the physician in diagnosis, who is responsible for making all final patient management decisions.
| Type of Use (Select one or both, as applicable) |
|-------------------------------------------------|
|-------------------------------------------------|
| <div style="display:flex; align-items:center;"><input checked="true" type="checkbox"/> Prescription Use (Part 21 CFR 801 Subpart D)</div> |
|-------------------------------------------------------------------------------------------------------------------------------------------|
| <div style="display:flex; align-items:center;"><input type="checkbox"/> Over-The-Counter Use (21 CFR 801 Subpart C)</div> |
### CONTINUE ON A SEPARATE PAGE IF NEEDED.
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."
{3}------------------------------------------------
# 510(K) SUMMARY
[In accordance with 21CFR 807.92]
#### 1. Submitter
| 510(k) Sponsor: | TereRecon, Inc. |
|------------------------|----------------------------------------------------------------------------------|
| Address: | 4309 Emperor Blvd., Suite 310<br>Durham, NC 27703, USA |
| Contact Person: | Patrick Willhite<br>Director of Quality Assurance and Regulatory Affairs |
| Contact Information: | Email: pwillhite@terarecon.com<br>Phone: 919.670.1539<br>Facsimile: 650.372.1101 |
| Date Summary Prepared: | 10/30/2020 |
## 2. Device
| Proprietary (Trade) Name: | Neuro.Al Algorithm (“Neuro.Al”) |
|---------------------------|------------------------------------------------------------|
| Common Name: | Medical Imaging System |
| Classification: | § 892.2050, Picture Archiving and Communication<br>System. |
| Product Codes: | LLZ – System, Image Processing, Radiological |
### 3. Predicate Device
| Predicate Device | iNtuition-TDA, TVA and Parametric Mapping (K131447) |
|------------------|-----------------------------------------------------|
| Reference Device | iNtuition system (K121916) |
### 4. DEVICE DESCRIPTION
The Neuro.Al Algorithm is a modification of the predicate device, iNtuition-TDA, TVA, Parametric Mapping which was cleared under K131447. The predicate device is an optional module/workflow for the iNtuition system (K121916). The Neuro.Al Algorithm is a standalone image processing software device that can be deployed as a Microsoft® Windows executable on off-the-shelf hardware or as a containerized application (e.g., Docker container) that runs on off-the-shelf hardware or on a cloud platform. The device has limited network connectivity or external medical support.
{4}------------------------------------------------
The Neuro.Al Algorithm allows motion correction and processes, calculates and outputs brain perfusion analysis results for static, functional, dynamic and derived imaging datasets acquired with CT or MRI. Neuro.Al results are used for visualization and analysis of dynamic brain perfusion image data, showing properties of changes in contrast over time. This functionality includes calculation of parameters related to brain tissue perfusion, vascular assessment displayed in rotational Maximum Intensity Projection (MIP) called the tumble view, and tissue blood volume and other parametric maps with or without brain ventricles included in the calculation.
Outputs include text and parametric map displays of measurements including time to peak (TTP), take off time (TOT), recirculation time (RT), mean transit time (MTT), blood volume (BV/CBV), blood flow (BF/CBF), classification maps, reformatted images and rotational MIPs for 2D and 3D visualization of brain tissues and blood vessels, and for correlation to the perfusion maps.
The results of the Neuro.Al Algorithm can be delivered to the end-user through image viewers such as TeraRecon's iNtuition system, TeraRecon's Northstar Al Results Explorer ("Northstar"), or other third-party image viewing systems like PACS that can display the DICOM results generated by Neuro.Al output does not depend on the viewing system's capabilities as the results are self-contained and the only interface is through DICOM.
When the Neuro.Al Algorithm results are used on iNtuition, all the standard features offered by iNtuition are employed such as image manipulation tools like drawing the region of interest, manual or automatic segmentation of structures, tools that support creation of a report, transmitting and storing this report in digital form, and tracking historical information about the studies analyzed by the software.
The Neuro.Al algorithm can be used by physicians to aid in the diagnosis. The software is not intended to replace the skill and judgment of a qualified medical practitioner and should only be used by individuals that have been trained in the software's function, capabilities and limitations. The device is intended to provide supporting analytical tools to a physician, to speed decision-making and to improve communication, but the physician's judgment is paramount, and it is normal practice for physicians to validate theories and treatment decisions multiple ways before proceeding with a risky course of patient management.
### 5. INDICATIONS FOR USE
The Neuro.Al Algorithm is an algorithm for use by trained professionals, including but not limited to physicians, surgeons and medical clinicians.
The Neuro.Al Algorithm is a standalone image processing software device that can be deployed as a Microsoft Windows® executable on off-the-shelf hardware or as a containerized application (e.g., a Docker container) that runs on off-the-shelf hardware or on a cloud platform. Data and images are acquired via DICOM compliant imaging devices. DICOM results may be exported, combined with or utilized by other DICOM-compliant systems and results.
The Neuro.Al Algorithm provides analysis capabilities for statio, functional, dynamic and derived imaging datasets acquired with CT or MRI. It can be used for the analysis of dynamic brain perfusion image data, showing properties of changes in contrast over time. This functionality includes calculation of parameters related to brain tissue perfusion, vascular assessment and tissue blood volume and other parametric maps with or without the ventricles in the calculation. The algorithm also includes volume reformat in various orientations, rotational MIP 3D batch while removing the skull. This "tumble view" allows qualitative review of vascular structure in direct correlation to the perfusion maps for comprehensive review.
{5}------------------------------------------------
The results of the Neuro.Al Algorithm can be delivered to the end-user through image viewers such as TeraRecon's Aquarius iNtuition system. TeraRecon's Northstar Al Results Explorer, or other image viewing systems like PACS that can support DICOM results generated by Neuro.Al.
The Neuro.Al Algorithm results are designed for use by trained healthcare professionals and are intended to assist the physician in diagnosis, who is responsible for making all final patient management decisions.
### 6. SUMMARY OF TECHNOLOGICAL CHARACTERISTICS
The Neuro.Al Algorithm is substantially equivalent to the predicate device, iNtuition-TDA, TVA, Parametric Mapping (K131447). It has the same intended use and the same basic technological characteristics as the predicate device. The main difference between the subject and predicate device is the standalone nature of the subject device.
Both the subject and predicate device allow motion correction and processes, calculates and outputs brain perfusion analysis results for static, functional, dynamic and derived imaging datasets acquired with CT or MRI. The results are used for visualization and analysis of dynamic brain perfusion image data, showing properties of changes in contrast over time. This functionality includes calculation of parameters related to brain tissue perfusion, vascular assessment displayed in rotational Maximum Intensity Projection (MIP) called the tumble view, and tissue blood volume and other parametric maps. The subject device can also display maps with or without brain ventricles included like the reference device, iNtuition system (K121916). The reference device includes segmentation functionality where the segmentation can be displayed or hidden for any part of the body, including brain ventricles.
Outputs include text and parametric map displays of measurements including time to peak (TTP), take off time (TOT), recirculation time (RT), mean transit time (MTT), blood volume (BV/CBV), blood flow (BF/CBF), classification maps, reformatted images and rotational MIPs for 2D and 3D visualization of brain tissues and blood vessels, and for correlation to the perfusion maps.
Both the subject and predicate devices are interoperable with CT and MR scanners, thirdparty hospital systems such as PACS, and the iNtuition platform. The subject device is a standalone software device, the results of which can also be consumed by TeraRecon's Northstar Al Results Explorer via EnvoyAl as the algorithm hosting platform or by other third-party image viewing systems that can display the DICOM results generated by the Neuro.Al Algorithm.
The differences in technological characteristics do not raise any new or different questions of safety or effectiveness. Software verification and validation testing and performance testing validate that the Neuro.Al Algorithm is as safe and effective as the predicate device to support a determination of substantial equivalence.
See the table below for a description of the technological similarities and differences among the subject, predicate, and reference devices.
{6}------------------------------------------------
| | Subject Device<br>Neuro.AI Algorithm<br>(TBD) | Predicate Device<br>iNtuition-TDA, TVA,<br>Parametric Mapping<br>(K131447) | Reference Device<br>iNtuition system<br>(K121916) |
|-----------------------------------------------------------------------------------------------------------|----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| Areas of Use | Same and other trained<br>clinical users | Radiology | Radiology |
| Modality Type | Same | Vendor-neutral - CT, MR and<br>other volumetric imaging<br>modalities. Images are<br>exposed over time. | Vendor-neutral - CT, MR,<br>Nuc, PET, Angio, US/Echo,<br>SPECT, CR/DR Review |
| DICOM®<br>formats | Same and DICOM 3.x | Yes, supports DICOM 3.0 | Yes, supports DICOM 3.0 |
| Operating<br>System | Same and<br>CentOS (Interoperability) | Microsoft<br>Windows® executable on<br>off-the-shelf hardware | Microsoft<br>Windows® executable on<br>off-the-shelf hardware |
| Body Part | Same | Head - entire brain or from<br>lower edge of the base of<br>nucleus to upper edge of the<br>ventricles. | Head and other regions and<br>organs within the body |
| Key<br>Functionality/<br>Features | 2D, 3D and 4D viewing,<br>multi-phase series<br>support, zoom, pan,<br>window level, rotate, cine<br>and display layouts and<br>templates | 2D, 3D and 4D viewing,<br>multi-phase series<br>support, zoom, pan,<br>window level, rotate, cine<br>and display layouts and<br>templates | 2D, 3D and 4D viewing,<br>multi-phase series<br>support, zoom, pan,<br>window level, rotate, cine<br>and display layouts and<br>templates |
| | ROI Markers: Ability to<br>create preset shapes or<br>freehand ROI for<br>measurements or<br>segmentations | ROI Markers: Ability to<br>create preset shapes or<br>freehand ROI for<br>measurements or<br>segmentations | ROI Markers: Ability to<br>create preset shapes or<br>freehand ROI for<br>measurements or<br>segmentations |
| | Arterial and venous input<br>function selection,<br>automatic and manual | Arterial and venous input<br>function selection,<br>automatic and manual | Arterial and venous input<br>function selection,<br>automatic and manual |
| | Ventricle segmentation | | Ventricle segmentation |
| Ventricle<br>Segmentation | Setting allows software to<br>display maps with or<br>without brain ventricles<br>included | This device is a module of<br>iNtuition. When used with<br>iNtuition, the segmentation<br>tools can be applied to any<br>part of the body, including<br>brain ventricles. | Editing and segmentation<br>tools are provided<br>including freehand crop,<br>cut, dynamic region grow,<br>bone removal tools, rib<br>cage removal, table<br>removal tools, and tools to<br>provide an initial selection<br>of bone or air-filled vessels<br>(e.g. lung or colon) for<br>removal or improvement.<br>Any segmentation can be<br>displayed or hidden and<br>this is applicable for any |
| | Subject Device<br>Neuro.Al Algorithm<br>(TBD) | Predicate Device<br>iNtuition-TDA, TVA,<br>Parametric Mapping<br>(K131447) | Reference Device<br>iNtuition system<br>(K121916) |
| | | | part of the body, including<br>brain ventricles. |
| Perfusion<br>measurements<br>and color maps | Same | Time to Peak (TTP) Take off Time (TOT or<br>Maximum Slope of<br>Increase) Recirculation Time (RT) Mean Transit Time (MTT) Blood Volume (BV/CBV) Blood Flow (BF/CBF) Perfusion Maps | Time to Peak (TTP) Take off Time (TOT or<br>Maximum Slope of<br>Increase) Recirculation Time (RT) Mean Transit Time (MTT) Blood Volume (BV/CBV) Blood Flow (BF/CBF) Perfusion Maps |
| Graph Displays | Same | Artery and Vein Fitted and<br>Raw curves - time/activity | Artery and Vein Fitted and<br>Raw curves - time/activity |
| Export Format | Same | DICOM format | DICOM format plus JPEG,<br>BMP, AVI, Word |
| Methods for<br>Mathematical<br>Modeling | Same | SVD | SVD |
| Arterial and<br>Venous Input<br>Function<br>Selection | Same | Automatic and manual | Automatic and manual |
| Interoperability/<br>Compatibility | CT and MR Scanners Third-party hospital<br>systems such as a PACS<br>server, EMR or other iNtuition Advanced<br>Visualization system Algorithm dockerization<br>using Docker™ hosted in<br>TeraRecon's EnvoyAl platf | CT and MR Scanners Third-party hospital<br>systems such as PACS<br>server, EMR or other iNtuition advanced<br>visualization system | CT and MR Scanners<br>plus other imaging<br>modalities Third-party hospital<br>systems such as PACS<br>server, EMR or other |
| Subject Device<br>Neuro.Al Algorithm<br>(TBD) | Predicate Device<br>iNtuition-TDA, TVA,<br>Parametric Mapping<br>(K131447) | Reference Device<br>iNtuition system<br>(K121916) | |
| Northstar Al Results<br>Explorer | | | |
| • Other image viewing<br>systems that can support<br>DICOM results generated<br>by the Neuro.Al Algorithm | | | |
| • Notification systems | | | |
# Table 1: Technological Characteristics comparison
{7}------------------------------------------------
{8}------------------------------------------------
# 7. PERFORMANCE DATA
Safety and performance of the Neuro.Al Algorithm have been verified and validated through software testing and performance evaluation. Software development and testing were performed in accordance with IEC 62304:2006/A1:2015, Medical Device Software - Software life cycle processes, utilizing a risk-based methodology. Risk has been evaluated in accordance with testing lso 14971:2007, Medical Devices - Application of Risk Management to Medical Devices. During software testing, all predefined acceptance criteria for the Neuro.Al Algorithm were met and all software test cases passed. The same verification and validation methodology, risk assessment and acceptance criterion were used for predicate device.
The results of the software and performance testing validate that the Neuro.AI Algorithm meets its qualified requirements, performs as intended, and is as safe and effective as the predicate device. No new or different questions of safety or efficacy have been raised as a result of the verification and validation process.
# 8. CONCLUSION
The Neuro.Al Algorithm is as safe and effective as the predicate device, iNtuition-TDA, TVA, Parametric Mapping module. The Neuro.Al Algorithm has the same intended use and the indications for use fall within the scope of that for the predicate device. Many of the technological characteristics are the same for the subject and predicate devices. Any differences in technological characteristics between the subject and predicate devices have been addressed through software verification testing and performance testing and do not raise any new or different questions of safety or effectiveness. Additionally, the differences in technological characteristics have been compared to a reference device which is currently legally marketed in the United States.
All risks were analyzed and no new risks, changes to existing risks, or new risk controls were identified as a result of the Neuro.Al Algorithm. The testing results and analysis above support a determination of Substantial Equivalence of the Neuro.Al Algorithm to the predicate device in terms of safety, efficacy, and performance.
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.